optimised design methodologies for...

This project has received funding from the European Union Seventh Framework Programme

under grant agreement n° 609349.

OPTIMISED DESIGN METHODOLOGIES FOR ENERGY-EFFICIENT BUILDINGS

INTEGRATED IN THE NEIGHBOURHOOD ENERGY SYSTEMS

eeEmbedded – D10.400

Final Project Report

Responsible Authors:

Peter Katranuschkov and Raimar J. Scherer (eds.)

Contributions: All project partners

Due date: 30.09.2017

Issue date: 17.09.2017

Nature: PU/CO

Coordinator: R. J. Scherer, Institute for Construction Informatics, Technische Universität Dresden, Germany

D10.400 Final Report

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© eeEmbedded Consortium www.eeEmbedded.eu

Start date of project: 01.10.2013 Duration: 48 months

Organisation name of lead contractor for this deliverable:

Coordinator: TECHNISCHE UNIVERSITAET DRESDEN, Institute of construction informatics.

History

Version Description Lead Author Date

0.1 Deliverable Structure Peter Katranuschkov, Romy Guruz (CIB) 31.05.2017

0.2 Input Chapters 1, 2 and sect. 7.2.1 Romy Guruz (CIB) 14.06.2017

0.3 Contributions to Chapter 7 A. Ton (EPM) 21.06.2017

0.4 Contributions to Chapter 7 G. Dangl (IAB), A. Sukumar (RIB) 25.07.2017

0.5 Contributions to Chapters 4 and 7 M. Löffler (BAM), S. Poloczek (STA) 08.08.2017

0.5 Contributions to Chapter 7 P. Stenzel (EAS), F. Forns-Samso (GRA) 24.08.2017

0.6 Contributions to Chapter 7 R. Schär (SAR), J. Kaiser (IET) 01.09.2017

0.7 Contributions to Part 2 A. Navas (CEM) 01.09.2017

0.8 Contributions to Chapters 4, 5, 8 M. Löffler (BAM), S. Poloczek (STA) 04.09.2017

0.9 Contributions to Chapters 3, 6, 7 K. Baumgärtel, H. Pruvost, T. Grille, P. Katranuschkov (CIB)

05.09.2017

1.0 First complete draft of the report P. Katranuschkov (CIB) 06.09.2017

1.1 Contributions to Chapter 7 E. Mrazek, R. Zellner (NEM), K. Solvik (DDS) 08.09.2017

1.2 Contributions to Chapter 3 G. Calleja-Rodriguez (CEM) 08.09.2017

1.3 Contributions to Part 2 B. Protopsaltis (SOF), P. Katranuschkov (CIB) 11.09.2017

1.4 Full text/gap check and editing P. Katranuschkov, R. Schülbe (CIB) 14.09.2017

1.5 Full final draft P. Katranuschkov, R. Scherer, R. Schülbe (CIB) 15.09.2017

2.0 Approved by Coordinator and uploaded to ECAS

CIB 18.09.2017

Copyright

This report is © eeEmbedded Consortium 2017. Its duplication is restricted to the personal use within

the consortium, the funding agency and the project reviewers. Its duplication is allowed in its integral

form only for anyone's personal use for the purposes of research or education.

Citation

Katranuschkov P. & Scherer R. J. (eds.), (2017): eeEmbedded Deliverable D10.400 - Final Project Report; © eeEmbedded Consortium, TU Dresden, Dresden, Germany.

Acknowledgements

The work presented in this document has been conducted in the context of the seventh framework

programme of the European community project eeEmbedded (n° 609349). eeEmbedded is a 48

month project that started in October 2013 and is funded by the European Commission as well as

by the industrial partners. Their support is gratefully appreciated. The partners in the project are

Technische Universität Dresden (Germany), Fraunhofer-Gesellschaft zur Förderung der

angewandten Forschung E.V (Germany), NEMETSCHEK Slovensko, S.R.O. (Slovakia), Data Design

System ASA (Norway), RIB Information Technologies AG (Germany), Jotne EPM Technology AS

(Norway), Granlund OY (Finland), SOFISTIK HELLAS AE (Greece), Institute for applied Building


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Informatics IABI (Germany), FR. SAUTER AG (Switzerland), Obermeyer Planen + Beraten (Germany),

Centro de Estudios Materiales y Control de Obras S.A. (Spain), STRABAG AG (Austria) and

Koninklijke BAM Group NV (The Netherlands). This report owes to a collaborative effort of the

above organizations.

Project of SEVENTH FRAMEWORK PROGRAMME OF THE EUROPEAN COMMUNITY

Dissemination Level

PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)


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TABLE OF CONTENTS

Executive Summary _______________________________________________________________________5

1. The project on a page _________________________________________________________________6

2. Vision, Mission and Goals of the Project __________________________________________________7

3. Top 10 Project Achievements ___________________________________________________________9

4. The eeEmbedded Holistic Design Methodology __________________________________________ 10

5. Pilot Demonstrators and Usage Scenarios _______________________________________________ 15

6. The eeEmbedded Virtual Lab Platform _________________________________________________ 20

6.1. Software Architecture __________________________________________________________ 20

6.2. Structure of the eeEmbedded Virtual Lab __________________________________________ 21

6.3. The eeEmbedded Multimodel Framework __________________________________________ 22

7. eeEmbedded Products and Findings ___________________________________________________ 24

7.1. Project Setup and Requirements Management Service ________________________________ 24

7.2. Design Tools _________________________________________________________________ 27

7.2.1. Architectural Design ______________________________________________________________ 27

7.2.2. HVAC Design ____________________________________________________________________ 30

7.2.3. BACS Design ____________________________________________________________________ 31

7.2.4. Energy System Modelling (ESIM) ____________________________________________________ 32

7.3. Scenario Manager _____________________________________________________________ 33

7.4. Multimodel Navigator __________________________________________________________ 35

7.5. Collaboration and Resource Management Services and Tools___________________________ 38

7.5.1. BIM—It ________________________________________________________________________ 38

7.5.2. EDM Model Server _______________________________________________________________ 39

7.5.3. Cloud Services __________________________________________________________________ 41

7.6. Model Validation Services _______________________________________________________ 43

7.6.1. Ontology-based Exchange Requirement Checking and Visualisation _______________________ 43

7.6.2. Ontology-based Key Point Checking and Visualisation ___________________________________ 45

7.7. Energy Simulation and Analysis Services and Tools ___________________________________ 47

7.7.1. Input Preparation ________________________________________________________________ 47

7.7.2. Thermal Simulations with TRNSYS___________________________________________________ 50

7.7.3. 3D Wind Analysis in Urban Design __________________________________________________ 52

7.7.4. 3D Therm Analysis in Detailed Design ________________________________________________ 54

7.8. LCA and LCC Analysis ___________________________________________________________ 56

7.9. Risk Assessment Service ________________________________________________________ 58

7.10. Multi Key Point Analysis Tool for Decision Making ____________________________________ 61

8. Evaluation of the eeEmbedded Platform ________________________________________________ 65

9. Exploitation of the eeEmbedded Platform ______________________________________________ 71

10. Conclusions _______________________________________________________________________ 73

Appendix _____________________________________________________________________________ 74

References ________________________________________________________________________ 74

Acronyms _________________________________________________________________________ 77

Partner Abbreviations _______________________________________________________________ 80


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Executive Summary

This deliverable report describes the overall final findings and achievements of the eeEmbedded project.

Publicly accessible via the project’s web site as well as via the EC web services, it gives an overview of the

project, presents the initial vision, mission and goals at the project start and explains the developed new key

point (KP) based holistic design methodology and the real pilot buildings used to validate, evaluate and

demonstrate the project developments and findings. It lists in concise form the main project achievements

and then goes into a more detailed presentation of the developed overall Virtual Energy Lab Platform and its

underlying Multimodel Framework and the component services and tools integrated in the platform for the

purpose of efficient energy-aware building design.

The report contains also a second part which is subject to confidentiality. It comprises a single Chapter

“Potential Impact”, structured in accordance with the predefined FP7 rules and providing an overview of the

project’s impact in terms of dissemination measures and activities, exploitable foreground and societal

implications.

All partners have contributed to the report under the lead of TUD-CIB as follows:

TUD-CIB: Report structuring and overall editing, main input to Chapters 1-5 and 10 as well as contributions

to Chapter 7 and Part 2

BAM: Main input to Chapters 6 and 8 and contributions to Chapter 3

CEM: Contributions to Chapters 3, 4, 7 and 8 and Part 2

SOF: Contributions to Chapters 7, 9 and Part 2

STA: Main input to Chapters 6 and 8

EAS, TUD-IET, NEM, DDS, RIB, EPM, GRA, IABI, SAR and OPB: Contributions to Chapter 7.

The partner abbreviations are shown in the Appendix to this report.


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1. The project on a page

eeEmbedded is an European FP7 industry-driven project established under call EeB.NMP.2013-5 “Optimised

design methodologies for energy-efficient buildings integrated in the neighbourhood energy systems”.

Project Budget: 11.388 M€

Duration: 4 years [01.10.2013 – 30.09.2017]

Main product: Collaborative design and simulation platform and related design methodology

The eeEmbedded consortium features a mix of 15 partners from 9 European countries, covering the whole

knowledge transfer chain and all key areas of research and development relevant to the project goals.

They represent 4 types of market segments:

End-users including construction, architectural and engineering companies as well as control systems

and equipment provider (Koninklijke BAM Groep NV, Netherlands; STRABAG AG, Austria; Centro de

Estudios Materiales y Control de Obras S.A., Spain; Obermeyer Planen + Beraten GmbH, Germany; Fr.

Sauter AG, Switzerland).

Software developers (Nemetschek ALLPLAN Slovensko SRO, Slovakia; Data Design System ASA, Norway;

Jotne EPM Technology AS, Norway; Granlund Oy, Finland; SOFiSTiK Hellas, Greece; Fr. Sauter AG,

Switzerland).

Research institutes with knowledge on BIM/IFC management and interoperability (Institute For Applied

Building Informatics, Germany) and control system modelling, numerical analysis and user activity

modelling (Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V., Germany).

Academic organization (TU Dresden, Germany, with the institutes Construction Informatics and Power

Engineering), specialized in BIM, SOA, ontologies, interoperability, cloud computing and related

orchestration management and in energy system modelling and numerical simulation of any related

system.

Coordinator of the project is the Institute of Construction Informatics of the TU Dresden.

Project Website:

eeEmbedded.eu

Project Career:

linkedin.com/company/eeEmbedded

Project YouTube Channel:

youtube.com/channel/UCkgcav2Q9Zbh

PzAY2a2sYwA

EC Contribution:

7.649 M€


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2. Vision, Mission and Goals of the Project

Already at the outset, when the proposal was prepared, clear vision, mission and goals of the eeEmbedded

project had been identified.

VISION

The project consortium set out to develop an open BIM-based holistic collaborative design and simulation

platform, a related holistic design methodology, an energy system information model and an integrated

information management framework for designing energy-efficient buildings and their optimal energetic

embedding in the neighbourhood of surrounding buildings and energy systems. In this environment, a new

design control and monitoring system based on hierarchical Key Performance Indicators (KPI) will support

the complex design collaboration process. Knowledge-based detailing templates will allow energy simula-

tions as soon as in the urban design phase, and BIM-enabled interoperability will provide for a seamless

holistic design process with distributed experts, and a seamless integration of simulations in the virtual

design office (energy performance, CO2, CFD, control system, energy system, climate change, user

behaviour, construction, facility operation), thus extending it to a real virtual design lab.

MISSION

The main products eeEmbedded (eeE) develops are:

A holistic design methodology based on a hierarchically structured dynamically evolving KPI system which

guides and monitors the progress of the multi-disciplinary, multi-model and multi-physics design process.

A collaborative, holistic design platform comprising a virtual energy design lab and a virtual multi-

disciplinary collaborative design office for the design and evaluation of new or retrofit buildings

embedded in their energetic environment and covering their energetic performance, their constructa-

bility and their energetic vulnerability on different levels of detail.

A new reference model schema for the Energy System Information Model (ESIM) structured according to

BIM-IFC (ISO 16739) and incorporating the topography structure of the energetic environment in

accordance with cityGML. This will complement BIM concerning the internal and external energy

systems, built on and subsuming the various proprietary data structures existing for energy systems.

An ontology-based open interoperability system as the baseline for managing the information and cross-

dependencies of the multi information models, BIM, ESIM and BACS, and the related multi-physics

problems and their computational analyses.

The vision of eeEmbedded was to increase, by an order of magnitude, the quality of energy-efficiency in

building design through the development of an In-Silico Energy Simulator Laboratory.

The mission of eeEmbedded was to develop ICT products and services that will provide for an efficient,

functional and easily configurable Virtual Energy Lab Platform based on an ontology-supported

interoperable BIM implementing established information management standards.


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A multi-model information management method and related management services enabling the

integration of the separately maintained domain models, combining them to multi-models wherever

needed, filtering out the right views with minimal amount of information, mapping the eeeBIM data to

the appropriate computational models and taking care of the interactions and feedback cycles for the

multi-physical interdependencies of the computational models with prototype implementation of the

building thermal energy system, the neighbourhood energy systems, natural ventilation and building-

wind interactions (cool out and wind tunnel effects).

A knowledge management system for fast and grounded design decision-making with characteristic design

detailing templates enabling quantitative analyses and simulations as soon as in the early design phases.

GOALS

Specifically, the project work was focused on the following 7 research and development objectives:

Interoperability of the design objectives as baseline for collaborative holistic design using a new KPI-

based design methodology; the new developed Key Points (KPs) shall act as guidelines for milestones

and control of the collaborative design process, providing the interoperability and monitoring the

progress of the design during its different phases.

Interoperability of the information for heterogeneous distributed information resources and services on

the basis of system and domain ontology schemas and tools

A knowledge structure over the information domain models providing the mapping information needed

to transform the domain information models to computational engineering models, as well as the

knowledge needed to decide on the necessary level of detail and the respective cross-model structure.

Holistic collaborative virtual design office based on collaborative and virtual enterprise methods to

manage people, tools and information, including a change management component to properly handle

the various changes arising during design and enabling to find, retrieve and compare multiple different

design alternatives with the help of an advanced BIM-based visualization system.

Holistic virtual design lab providing quantitative computational support based on efficient cloud computing

methods; the tools and services integrated in the virtual design lab will provide the computer power for the

various required engineering analysis and simulation tasks and will support the feedback cycles for the

interrelationships between the computational models, based on the interoperability of system information.

Stochastic approach as part of the overall virtual design lab approach, extending current deterministic

models and approaches to cope with the uncertainties in the lifecycle concerning climate, energy

provision, as well as the usage of the building and the human behaviour.

Knowledge-supported design methods enabling fast preliminary detailing in the early design phase. This

is a mandatory issue to be able to carry out computational analysis and simulations and to obtain

conclusive quantitative results about the energetic performance of the building and about the

vulnerability of the energy systems already at the early design stages, when fundamental energetic

design decisions have to be made that are hardly alterable later on.

The main objective of the project was to develop ICT building blocks based on standardised Building

Information Models that will integrate, complement and empower existing tools for design and

operation management to the envisaged Virtual Energy Lab.


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3. Top 10 Project Achievements

We consider the following results as Top 10 achievements of the eeEmbedded project:

Key Point based design method supporting the control of the collaborative design work and the

monitoring of the progress against the design objectives.

Open ICT platform incorporating the Scenario Manager for process management, the Multi-Model

Navigator for checking the models and assigning templates, and an extensible number of simulation and

analysis services and a KP analysis tool for decision making grouped in interlinked configurable Virtual

Energy Labs for each role and domain in the design process.

Structured Key Point setup enabling definition of performance objectives and requirements in holistic

and automated manner for verification and validation of the design objectives continuously, at all key

points throughout the design process.

Scenario Manager (ScM): This brand new prototype application supports the collaborative project

process by dynamically assigning and monitoring project tasks and attaching the required data and

actions to them. In this way, project work can proceed in coordinated manner with clearly allocated

responsibilities, work items and interdependencies for each member of the project team.

Project collaboration via consistent use of the BIM Collaboration Format (BCF): The integration of the

BCF platform in the ScM ensures effective communication in the project team. The successor in the

process is informed about the finalisation of the previous task and receives links to all the data

necessary for his/her task.

Easy creation and evaluation of variants: The eeEmbedded platform incorporates generic climate,

occupancy, construction, HVAC, BACS and maintenance templates which can be assigned to element

types, to analyse the impact of various parameters on the sustainable performance of a building.

Multi-Model approach: For most of the simulation tools a Multi-Model Container (MMC) was imple-

mented, which means that only one IFC file is needed per alternative, whereas various external information

sources and various variants are linked to elements in the IFC file using a variation matrix. E.g. for 50 design

variants there is no need to create 50 IFC models but only one model with 50 different variants linked to it.

Exchange Requirements (ER) checking: The Exchange Requirements which are crucial for collaboration

in the project team can be checked via the Ontology Verification Service (OVS) and missing information

highlighted in red in the BIM model. The service is directly integrated in the Scenario Manager, where

ER are linked to the specific tasks in the process.

BIM-based Interoperability APIs: In the eeEmbedded platform simulations and analyses run as services.

This means that the results of various variants are processed without interaction with the end user.

Manual work is significantly reduced via the developed APIs. The coupling of data and tools becomes

feasible and the pre-processing time for model preparation is brought to minimum, thereby enabling

affordable variant examination.

Just-in-time result evaluation and decision-making: The developed new decision-making tool analyses

the impact of Key Design Parameter (KDP) on sustainable Key Performance Indicators (KPI) such as

energy, emissions, thermal comfort and Life Cycle Costs (LCC). In addition, the impact of risks can be

analysed. The results are weighted based on the preferences and visualized in a decision value graph,

which makes the comparison of alternatives and variants very efficient and transparent.


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4. The eeEmbedded Holistic Design Methodology

eeEmbedded has developed a new ICT based intelligent methodology to design energy efficient buildings

integrated in their neighbourhood. It extends existing BIM developments with a multi-model approach for

modelling and analysing the building architecture and its systems (HVAC, BACS) considering facility

management strategies and integrating sophisticated simulations to optimise energy, costs and

environmental impact along the life-cycle of the building.

The design methodology was specified by the end-users of eeEmbedded at the beginning of the project

within the Deliverables D1.2 (Geissler et al., 2014), D2.1 (Guruz et al., 2015a, b). These specifications were

used by eeEmbedded developers as basis to implement the supported tools and services integrated in the

platform. After the implementation of the supported tools and services, the end-users have used them for

the design of two pilot buildings (Sprenger & Poloczek, 2017a, b). Finally, the developed methodology for

holistic energy efficient building design has been updated taking into account the implemented tools and

services and the experience from the performed pilot projects where the implemented tools and services

and the design methodology were used.

The eeEmbedded Methodology features a clear BIM-based approach. It uses Key Points to drive the design,

and Templates to facilitate and accelerate the process. The Key Points are measurable target values that

represent requirements coming from clients, regulations, site and designers. They are used as basis for

taking informed decisions on design solutions. The Templates contain valuable information to speed-up and

streamline the design process while sophisticated analysis methods are applied to a large number of design

variants and alternatives making use of cloud technology.

The major aspects of the developed methodology that distinguish it from state-of-the-art design work are as

follows:

Investing time on project setup

A comprehensive project setup is required to ensure integrated process, information flow and software in

BIM-based collaborative design and to avoid problems due to differences in working cultures, work

processes and software applications.

The more detailed the project setup is, the more straight forward the design process will be later on and the

more problems can be avoided in advance.

The project setup must precisely define:

Project Type in terms of building type, project address, contract type, budget and duration apart from

the project name and its description.

Project structure, i.e. the teams that will be involved and their roles.

Project infrastructure including the software that will be used and the required format of inputs and

outputs (IFC4, CSV, etc.).

Project requirements coming from sites, regulations and building owners; they must be described,

classified and aligned per domain and cross-domain as well as prioritized.

Key Points. The requirements must be translated into four types of measurable target values, also

known as Key Points: (1) Decision Values, (2) Key Performance Indicators, (3) Key Risk Indicators and

(4) Key Design Parameters. After detailing the Key Points, they must be linked to specific instants or

milestones of the design process (check points).


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Process setup, i.e. the definition and interrelationship of the major design tasks and their sequences as

well as the responsible actors.

Exchange Requirements, i.e. the information to be exchanged between the actors (inputs and outputs of

tasks).

The added value for AEC companies is the structured monitoring of all essential project requirements, which

requirements are verified and by whom. In this way it is possible to know whether the project is progressing

within the sustainable requirements and the budget which is an enormous support for quality control in

complex projects. The added value for the client is that s/he has continuous and transparent proof that all

the requirements are met in the project.

Using Key Points in structured ICT supported manner

The eeEmbedded methodology defines a comprehensive framework including rating scale and rules with the

end targets in mind. The basis is translating requirements and design criteria into Key Points which provide

verifiable design check points in form of target values. Key Points have two main missions. On the one hand,

they integrate all design criteria and requirements. On the other hand, they guarantee easy and fast

evaluation and comparison of design options based on relevant well-defined metrics.

Figure 1: Top down decomposition of requirements into Key Points and bottom up verification of Key Points

This means that already in the project brief of a project we need to develop the requirements and the major

design criteria top down according to the following steps:

Choose a Decision Value (DV) and set a target; e.g. sustainable score 85 or Internal Rate of Return of 5 %.

The Decision Value represents the preferences of the decision makers related to the project goals. It allows

prioritizing Key Performance Indicators by means of weighting factors (e.g., Passive House).

Define Key Performance Indicators (KPI) influencing the DV in order to be able to comprehensively

validate if the building is defined according to the clients’ needs. Prioritize those by weighting factors

and set targets based on a requirements analysis. Key Performance Indicators (KPIs) are numeric metrics

of building performance related to energy usage. They are influenced by Key Design Parameters and are

additionally the basis values for evaluation via Decision Values (e.g. cooling demand ≤ 15 kWh/m2a).


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Define Key Risk Indicators (KRI) and set target values that represent deviation of the building

performance due to the stochastic nature of various parameters such as risks related to energy use or to

the reliability of the energy system.

Define Key Design Parameters (KDP), and set targets based on a requirements analysis. Some Key Design

Parameters are already defined and act as constraints in the design process, whilst other parameters can

be adapted to optimise sustainable targets. Key Design Parameters (KDP) represent the required building

properties and usually have a limited value range (e.g. Uwall ≤0,15 W/m2K).

Figure 2: Overall procedure for the use of Key Points

Structured arrangement of processes, tasks, roles and information exchanges

Implementing the Building Information Modelling approach requires a clear arrangement of processes, tasks,

roles and information exchanges. The following questions must thereby be answered.

Who needs the information extracted from the building information model?

At which point in time this information is needed?

Which minimal amount of data has to be exchanged?

eeEmbedded recommends to digitally capture the information exchanges according to the IDM approach

(ISO 29481) and to steer and track them during the project.

To carry out this recommendation, exchange requirements are defined for the various BIM uses/phases and

configured for the specific project at hand in its digital environment. The developed Scenario Manager (ScM)

provides the functionality to setup and run the exchange requirements verification with the help of the

integrated Ontology Verification Service (OVS) and/or the externally used IfcDoc tool from buildingSMART.

Using Design Templates

Speeding up and streamlining the design process while integrating sophisticated analysis methods applied to

a higher number of design variants and alternatives within a shorter time works only when smart re-usable

templates are integrated into the design process. These templates are dividable into two types, (1) design

content templates and (2) process templates.

Main target of the template usage is the higher level of formalisation and the reproducible quality of design

and analysis results across projects and domains, the higher productivity of the involved design team, and

the highly improved capability to define and examine different design variants.


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Integration of both template types into design process starts with the setup of the project while using

process templates covering the step-by-step workflow including the targeted output quality and covering the

related exchange requirements between the process steps. The project setup should also cover the

agreement between the involved project partners about content templates. Typical examples for content

templates are pre-defined construction templates for building elements (walls, slabs, windows) and

equipment (HVAC, sensors and other building automation devices), and/or occupancy profiles as inputs for

both, the design and the analysis steps.

Figure 3: Content templates

Planning an urban design phase focused on energy

Urban design is crucial to take advantage of energy saving potential by using passive heating, photovoltaic

potential or natural ventilation. For example, it is widely known that the level of solar exposure which is

strongly linked to orientation and surrounding shadows influences heating, cooling and artificial lighting

needs of buildings.

In this line, it is important to create a trend to incorporate urban design and planning concepts into the building

design process in order to integrate them energy efficiently in their neighbourhood. That is why we

recommend having a first design phase focus on the urban design that takes into account energy

considerations. On the other hand, it is also crucial to include simulations and precise calculations as much as

possible into this first phase to increase the reliability of the decisions taken in the early stage of the design,

which are considered as the most influential decisions on the building performance (McArthur et al. 2014).

The urban design phase should thereby be focused on the development and evaluation of several building

cubature and energy supply concepts taking in consideration the integration into the neighbourhood and

the use of renewable or conventional resource on site and on district level. To this end it should include:

CFD simulations related to the climatic conditions (wind) to optimise thermal comfort and analyse the

integration of the building into the neighbourhood.

Energy building simulation to rank cubature variants in order to find the optimal energy demand

Taking into account uncertainties when ranking combinations of cubature and energy supply variants that

optimise energy consumption and use renewable or conventional resources on site and district level.

Life Cycle Cost analyses to also compare building cubature variants in combination with energy system

variants and the life cycle costs.


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Involving FM experts in the early phases of design

Traditionally, facilities management and property management (FM/PM) has been regarded with a poor

relation to the architecture, engineering and construction (AEC) industry. The eeEmbedded methodology

acknowledges that the role of FM is much wider than being involved only in the operational phase.

Therefore, FM/PM professionals’ involvement in the early phases of design and knowledge sharing

strengthens the integrated design process developed in the eeEmbedded project.

The involvement of FM professionals is perceived of high value for the entire facility lifecycle. Benefits are

seen in ensuring less rework, emphasizing value for money by taking into consideration life cycle aspects

such as LCC, LCA, investment costs, maintenance cost, flexibility, adaptability and environmental policies.

To date the use of BIM is not common for FM/PM professionals. However BIM tools are capable of

simulating and predicting different performance parameters that are very useful for the decision making

process at a strategic and operational level. FM/PM professionals do not need to necessarily know how to

perform the simulations but should be able to interpret and evaluate the results in form of key performance

indicators (KPIs) for making informed decisions in the long term. Potential KPIs generated for FM are related

to cost of maintenance and operation, revenue, space management, and environmental and safety issues.

These KPIs are integrated in the decision making methodology and supportive decision making tools used

during the design phases.

The methodology was elaborated for 3 design phases:

The urban design phase is focused on the development and evaluation of several building cubature and

energy supply concepts taking in consideration the integration into the neighbourhood and the use of

renewable or conventional resources on site and on district level. It comprises 7 tasks: project setup, design

cubature, CFD wind simulation, energy concept, lifecycle costing, energy simulations and overall decision

making.

The early design phase is focused on the modelling and analysis of combinations of construction types - for

rooms and building elements - and concepts for the HVAC system, the Building Automaton and Control

Systems (BACS) and Facility Management to optimise energy, environmental impact and costs along the life

cycle of the building. It encompasses 9 tasks: update project setup, develop construction types, develop

HVAC types, develop BACS types, FM concept, energy simulation, Lifecycle assessment analysis, LCC

calculation and overall decision making.

The detailed design phase is focused on the accurate development and analysis of room layout, combined

with the detailed design in terms of location and product features of HVAC systems, BACS systems and FM

strategies to optimise energy, environmental impact and costs along the life cycle of the building. It

encompasses 10 tasks: update project setup, create architecture product alternatives, create HVAC product

and location alternatives, design sensor and actuators network, enrich product alternatives with FM

information, energy simulation, CFD simulation, LCC calculation, LCA calculation and overall decision making.

A comprehensive description of the various aspects of the methodology and the developed design scenarios

is presented in Deliverable D2.5 “New ways of holistic working for energy optimized and embedded

building” (Calleja-Rodriguez et al. 2017).


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5. Pilot Demonstrators and Usage Scenarios

Two different pilot models of real buildings – W2 Leidsche Rijn (Figure 4) and Z3 (Figure 5) have been used to

verify and validate the platform and the KP methodology. Both pilots are office buildings, but with different

surroundings, architectural alignment with respect to other buildings and different working space inside as

well as different energy demand. The prepared initial CAD models were exported in the standard Industry

Foundation Classes (IFC) format and reflected as much as possible the necessary exchange requirements

specified in the Project Setup. The surroundings were modelled for the purpose of wind analysis using

computational fluid dynamics (CFD) and thermodynamic energy simulations.

Figure 4: eeEmbedded Pilot Demonstrator: W2 Building, Leidsche Rijn, BAM, Netherlands

Figure 5: eeEmbedded Pilot Demonstrator: Z3 Building, Züblin, Stuttgart, Germany)


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In accordance with the developed design methodology the two models were used to test and evaluate the

eeEmbedded virtual lab platform for the three targeted design phases: Urban Design, Early Design and Detailed

Design. Most comprehensive tests have thereby been performed for the Urban Design phase in order to verify

the holistic design goal of combining architectural and energy system (neighbourhood) design and the use of

energy and life cycle analyses and simulations as early as possible in order to facilitate optimal decision making.

Urban Design

In the Urban Design phase, the models are prepared as simple shapes. They consist of simplified spaces as

office, garage, staircase and slabs indicating the floors. The envelope is defined with external walls and a roof.

The end users proposed also three geometrical alternatives for each model. These differ with regard to shape,

orientation and/or size. Each of the alternatives was simulated with different variants of architecture and

energy systems. The specified variants are shown in Figure 6 below. They ensure that the evaluation is done

based on diverse configurations to have rich and diverse visual outcome demonstrating the flexibility, the

efficiency and the added value of the eeEmbedded methodology and the implemented ICT platform. The result

of the Urban Design phase reveals the best alternative to be further detailed and used for Early Design.

Figure 6: Urban design variants (Left: W2 Building Leidsche Rijn, Right: Z3 Building, Züblin, Stuttgart)

The design scenario for Urban Design is presented in Figure 7. During the first step of this scenario, setup for

the particular project within the Scenario Manager (ScM) of the eeEmbedded platform is prepared. The

project manager introduces information about the project, sets up the project process maps and assigns

project partners to the particular tasks. Decision Values (DV), Key Performance Indicators (KPI) and Key

Alternatives-IFCs

A1 (H Shape) WWR (NE & SW side) Heating System

40% District heating

60% Heat pump

U-Value Wall

Heavy - Brick: 0,19 W/m2.K

Leightweight - Steel: 0,15 W/m2.K

U-Value Window

2-pane: 1,10 W/m2.K

3-pane: 0,66 W/m2.K

A2 (U Shape) WWR (NE & SW side) Heating System


60% Heat pump

U-Value Wall



Shell

2-pane: 1,10 W/m2.K

3-pane: 0,66 W/m2.K

A3 (Rectangular) WWR (NE & SW side) Heating System


60% Heat pump

U-Value Wall



Shell

2-pane: 1,10 W/m2.K

3-pane: 0,66 W/m2.K


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Design Parameters (KDPs) are defined within the ScM. Architectural BIM models with alternatives are

created and uploaded as IFC files to the ScM. An Ontology Verification Service (OVS) is used to verify the

exchange requirements for the models and detect deviations. In addition, KDPs are checked visually in the

Multimodel Navigator (MMNav) accessible to all design team members via the Internet. End users assign

building occupancy and climate templates in MMNav and create variants by using construction and energy

system templates. Such templates can be assigned automatically using OmniClass classification or manually

to each building element. The next step is the CFD wind simulation, where wind flows are analysed and

alternatives which do not meet the set KPIs are either discarded or a message is sent to the designer to

optimise. Simulations are optionally done on stochastic scenarios in order to check design variable ranges

and uncertainties. The results from this process are sent further to the energy expert using the platform’s

services and the building collaboration format BCF (see Chapter 6 below).

Figure 7: Urban Design Scenario

Thermal simulation is performed using the same templates and variants as the CFD simulation. Energy

systems are analysed in TRNSYS for each alternative, including uncertainty analysis. The results are

transferred as BCF message with attached Multimodel Container (MMC) to the merging tool developed as

add-on of the EDM model server where all the information from templates and simulations is incorporated

into one BIM-IFC file. This file is transferred to the cost expert for the purpose of LCC calculation. In

iTWO/LCC cost templates are respectively selected and results are automatically generated from the data in

the IFC model. Finally, KPIs are checked and the information is forwarded for decision making. Granlund’s

Key Point Analysis (KPA) tool compiles all the results and presents them in a graphical way which provides

the designers with a clear overview of the best combination of a model alternative and a system or

construction variants that are the most suitable to use for energy-efficient design of an embedded building.


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Early Design

In the Early Design phase, the Level of Detail (LoD) of the selected best Urban Design alternative is increased.

Detailing of rooms and windows, exterior and interior doors is done and the space use and names of the

rooms as well as occupancy profiles are input. Materials of walls and floor construction are specified and

more detailed variants for exterior wall insulation, construction, glazing, HVAC and BACS were defined with

the aim to examine and evaluate the differences in energy performance, Life Cycle Costing (LCC) and Life

Cycle Assessment (LCA).

Selected Alternative Selected Alternative

Figure 8: Early design variants (Left: W2 Building Leidsche Rijn, Right: Z3 Building, Züblin, Stuttgart)

The design scenario is basically the same as for Urban Design (see Figure 9). However, after the thermal

simulation with TRNSYS, the HVAC concept is elaborated with successive ER and KP checks. Consequently, in

the MMNav, HVAC maintenance variants are set up and the eeBACS wizard is used to define the building

Alternatives-IFC Architecture HVAC BACS

A3 Exterior Walls_Insulation Material Heating & Cooling System Classification

Concrete block: 20 cm,

Mineral wool: 26 cm - 0,15 W/m2.K

Ceiling Induction A- High energy performance building automation

and control system (BACS) and technical building

management (TBM)

Concrete block 20 cm, Vacuum

panels + EPS: 8 cm - 0,15 W/m2.K

Concrete Core Activation B- Advanced BACS and TBM

Exterior Walls_Cladding Ventilation System

External cladding material: Stone

(Granite)

Ceiling Induction Unit

External cladding material: Metal Floor diffusors

20

cm

8 c

m

Ext

ern

al

Fin

ish

20

cm

26

cm

Ext

ern

al

Fin

ish

6 c

m A

ir g

ap

4 c

m G

ran

ite

20

cm

8 c

m

6 c

m A

ir g

ap

0,50

cm M

eta

l

20

cm

8 c

m


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automation and control systems. After that, energy simulations are performed, the results are merged by

the EDM server and LCC and LCA calculations are undertaken. Ultimately, all results are compiled and

evaluated with the help of Granlund’s KPA tool.

Figure 9: Early Design scenario

Detailed Design

Detailed Design follows the same steps as Urban and Early Design. It includes additionally thermal comfort

conditions estimation based on CFD simulation which follows the energy simulations. Comfort simulation is

done by using the 3DThermCFD tool developed in the project. The detailed geometry data is imported from

the enhanced BIM-IFC model. Operation points of the HVAC system and airflow conditions are specified

taking into account the desired usage scenarios, e.g. hot day during summer or the coldest day in winter.

Among others thermal and cooling loads are included from the templates. Energy simulation provides the

wall temperatures and solar gains from glazing surfaces. Also, BACS aspects are included. Detailed results

from the CFD simulation are presented via videos and 3D representations of suitable flow quantities are

shown as stream lines. These can be further attached to the IFC model in the MMNav.

The next Chapter 6 gives an overview of the overall eeEmbedded Virtual Lab Platform and its underlying

Multimodel Framework. Chapter 7 describes the integrated new or extended ICT services and tools and the

related major project findings enabling the use of the developed methodology in the industry context.


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6. The eeEmbedded Virtual Lab Platform

The eeEmbedded ICT platform comprises many end-user applications and service components which support

the design methodology in different ways. The combination of design tools, analysis tools, data tools and result

evaluation tools together with data servers led to the concept of an eeE Virtual Laboratory that can be

configured for the different goals and technical domains of the different members of the design team.

Combining such laboratories in a flexible environment encompassing data and compute cloud servers linked

together via a common service bus enables performing holistic energy studies about the designed buildings in

different design phases, starting already from the urban and early design stage.

6.1. Software Architecture

Some tools and components of each Virtual Lab are local software applications which import and export data

files like CAD or FM design. Some others are web components which provide REST-APIs. Therefore, the

software architecture is arranged as a service-oriented architecture (SOA) as shown in Figure 10. It is divided

in multiple layers:

User Layer: The eeEmbedded platform supports users from different domains. Each user has a specific

view on her/his data. The data are created and prepared for further usage in such views. Therefore, on this

layer role/domain specific tools are configured individually.

Virtual Lab Layer: The Virtual Lab Layer is responsible for providing the data connection among the

platform’s users, services and tools. This layer provides also interfaces to additional external tools which

are currently not compliant with tools from the user layer. Advanced BIM Tools like the Scenario

Manager (see Section 7.3) are aligned here to enable BIM workflows, data imports, file management and

building analyses.

Communication Layer: The communication layer provides the overall collaboration infrastructure. All

information and data files are shared within a Multimodel Container with the BIM—it collaboration

server via the Internet using the BCF approach (see Section 7.5.1).

Shared Service Layer: This layer manages the BIM workflows with its messages, events and tasks.

Workflow definitions created in the Scenario Manager are analysed by a workflow engine. This engine

informs the users when they have to do their work and what they have to do.

Service Component Layer: All external applications that help to handle data are aligned in that layer.

They can be called within the Scenario Manager workflows. Furthermore, the clients to web data

repositories and simulations are provided.

Repository Layer: The repository layer includes all data servers like BIM servers and template

repositories. While storage clouds are used to upload and download files to BIM servers, all simulations

run in compute clouds (Section 7.5.3) in order to allow analysing and evaluating hundreds of design

variants in parallel for the time of a little more than a single one.

The advantage of this layered SOA approach for the eeE Virtual Lab is the possibility to exchange

components in each layer without affecting components of other layers. Therefore, it is possible to use e.g.


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other BIM servers than the EDM model server (Section 7.5.2), other simulation tools than TRNSYS (Section

7.7.2) or another BIM management server than BIM—it (Section 7.5.1). This guarantees a fully flexible

system without relying on some special tools.

Figure 10: The eeE software architecture

6.2. Structure of the eeEmbedded Virtual Lab

As shown in Figure 10 the Virtual Lab Layer of the eeEmbedded platform comprises multiple virtual labs

which can be configured specifically for the needs of the separate domains and roles in the holistic design

process. In the scope of eeEmbedded the following views and virtual lab configurations have been

implemented:

Project Manager / Decision Maker

Architect

HVAC Designer

BACS Designer

Energy System Expert

CFD Simulation Expert

LCC Expert


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Further roles can be added using the developed architecture and methodology. Therefore, as a whole a

Virtual Design Office is established. One of its important features is that, while differently configured, all

virtual lab instances follow the same principal structure which enables their seamless integration into a

coherent overall platform. This unified structure comprises three layers:

User Layer, featuring general purpose or domain-specific authoring CAD tools, any other local domain

tools that require user interaction and the mandatory Scenario Manager (ScM) and Multimodel

Navigator (MMNav)

Analysis Layer, which bundles the interfaces to simulation and analysis services allocated on the

compute cloud of the platform

Core Layer, encompassing the needed collaboration, model validation and model management services

which are the same for all Virtual Lab instances and provide the functionality for their proper functioning

and interoperability within a consistent overall system. This layer includes also the APIs enabling the

interaction with the Service Bus and the underlying shared service layer of the platform (see Figure 10).

The next Chapter 7 describes the individual services and tools in the current realisation of the eeEmbedded

platform. With the exception of Section 7.5, which focuses on the infrastructure services “below” the service

bus, it presents the major exploitable components of the implemented Virtual Labs comprising the

eeEmbedded Virtual Design Office. The following Table 1 provides and overview.

Table 1: Implemented Virtual Lab tools and services by role and domain

Task / Tool / Service Virtual Lab Configurations

Project/BIM Manager

Architect HVAC Designer

BACS Designer

Energy Expert

CFD Simul. Expert

LCC Expert

Project Setup Service + (+) (+) (+) (+) (+) (+)

Arch. Design (Revit, Allplan … ) +

HVAC Design (DDS-CAD … ) +

BACS Design (eeBACS Wizard … ) +

BIM Checking (FZK, EDM, Solibri Viewers … ) + + + + +

ESIM Design Tools +

Scenario Manager (ScM) + + + + + + +

Multimodel Navigator (MMNav) + + + + + + +

ER / KDP Checking Services + + (+) (+) + + +

Thermal Simulation (TRNSYS) +

CFD Wind Simulation (3D Wind) +

CFD Thermal Simulation (3D Therm) +

LCA / LCC Analysis (iTWO LCA/LCC Plugin) +

Decision Making (Multi KPA Tool) + (+) (+) (+) (+) (+) (+)

6.3. The eeEmbedded Multimodel Framework

The outlined eeE Virtual Lab platform provides a flexible data concept, which allows sharing any needed

information in any data format using a concept called Multimodel Container (see Figure 11). Such a container

defines a structure that links different kinds of domain models via connections specified in Link Models.

Domain models are treated as independent information resources with their potentially own application

domain, data schema and data formalization. In this way Multimodels can be applied on any domain.


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There are several ways to realize the Multimodel Container (MMC). It can be implemented as compressed

archive file which contains all models or as a Multimodel that contains only the links to the actual resources.

In principal, Multimodel containers contain domain models from different domains and each model can be

independently processed by the project partners and their respective domain tools. Each partner can

thereby create or develop her/his own domain models and link them with existing models. This enables the

design partners to recombine the Multimodels based on their demands and the requirements of the project.

In eeEmbedded we have chosen this

concept because of the many

different proprietary data models the

involved tools need. The advantage is

that not all necessary data is added

to a single data model. This prohibits

redundant information in the overall

workflow, reduces model complexity

and minimizes the shared and stored

data. Hence, the eeE concept is not

to save all information in one huge

overarching model, more precisely

the building information model

expressed via IFC.

Figure 11: Multimodel container concept

We keep each file format as it is and interlink them with each other. Every user of such a Multimodel

container selects the domain models which are needed for her/his work. The tool which consumes the

Multimodel has to parse the link models to retrieve the expressed information space. Basically, in the

context of eeEmbedded a MMC provides the following structure with directories and files:

Multimodel [directory] - root directory

o bpmn [directory] - directory for BPMN workflows

MetaProcess.bpmn - BPMN workflow for the eeEmbedded meta process UrbanDesign.bpmn - BPMN workflow for the urban design phase EarlyDesign.bpmn - BPMN workflow for the early design phase DetailedDesign.bpmn - BPMN workflow for the detailed design phase

o keypoints [directory] - directory including the key point setup files

o links [directory] - directory including the link model files

o models [directory] - directory for all domain model files (BIM-IFC alternatives

templates, stochastic samples, occupancy profiles, climate

profiles)

o Multimodel.xml - meta information of all content in the Multimodel

o VariationModel.xml - information about all defined variants of a specific task.

The Multimodel is packed in ZIP format and attached as BIM snippet to BCF topics as explained in section

7.5.1. It is distributed via the ScM and most information is collected through services of the service

component layer (see Figure 10) from user applications like the Multimodel Navigator (MMNav), presented

in Section 7.4 below. The way how to retrieve information out of a MMC is described in more detail in

Section 7.3. During the eeE workflow the Multimodel information is increasing and all data files are stored

on BIM servers to be able to restore previous versions of BIM information.


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7. eeEmbedded Products and Findings

7.1. Project Setup and Requirements Management Service

The definition of all requirements is performed in accordance with the developed new design methodology.

It is supported by a newly developed user interface providing a front-end to the underlying ontology

framework of the eeEmbedded platform. Utilising VBA, XML, RDF/OWL and JAVA technologies, this

Microsoft Excel based user interface functions as editor for the generation and management of the ontology

models. The choice for Excel has as main reason that it is the best known and accessible spreadsheet tool

people use in general in their working environment. In that way, any project manager, BIM manager,

decision maker or owner can start using the interface and can easily and rapidly get into it.

Figure 12 shows schematically

the main parts of the interface

and the main steps to follow for

accomplishing a complete

project setup. The interface is

composed of several spread-

sheets, each related to some

requirement category and

representing a specific setting.

These categories decompose

requirements into:

Qualitative requirements

i. e. building functions de-

fined and selected using

the OmniClass classifica-

tion system,

Selection of generic pro-

duct libraries used conco-

mitantly for both building

design configuration and

for simulations,

Quantitative requirements

expressed in terms of Key

Point TO-BE values,

Design process settings for

establishing phases and

tasks,

Exchange requirements for

specification of mandatory

eeBIM data exchanges.

Figure 12: Workflow for the overall project requirement setup

Type categories, which are important for later automated template assignments and data filtering, are defined in

a first step. They are organized in five abstraction levels: District, Site, Construction, Space and Element.


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The District Level describes the available internal and external energy supply for the planned building.

This includes power plants as well as energy storages and the related values for energy consumption and

energy generation.

On the Site Level the building site of the planned building is described. The different areas, like parking

areas and areas for energy generation (e.g. area for solar panels), are detailed on this level.

The Construction Level contains the description of the different construction types like the type of the

building (e.g. High School, Police Station, Hospital, etc.) and other constructions.

On the Space Level the different spaces within the building are typified. This way certain Ifc space

elements can be related to a Restroom, Office Room, Corridor, etc.

The Elements Level represents the lowest level and contains various building element types.

By the definition of Key Point requirements the end user can address the four KP types introduced in the

methodology, i.e. decision value (DV), key performance indicators (KPI), key design parameters (KDP) and

key risk indicators (KRI). Figure 13 shows the DV setup view allowing to evaluate the designed building with

regard to owner’s preferences or to targeted certification schemes like LEED, BREEAM, DGNB, etc. (Guruz et

al. 2015b). More details about the DV definition are available in (Forns-Samso et al., 2015).

Figure 13: Spreadsheet dedicated to the configuration of the decision value (DV)

Figure 14 below shows the configuration sheet for the next setup level where all KPIs used in the project for

assessing performance criteria can be configured. These KP definitions are commonly used by certification

schemes and as usual metrics in civil engineering. For the pilots a fixed list of KP definitions was used, but it

is foreseen that in other practical applications such definitions would be extended and used as part of

companies’ knowledge. The same layout has been used for the three KP levels i.e. KPIs, KDPs and KRIs.

Figure 14: View of the sheet used for entering and setting KPI requirements.

https://dict.leo.org/german-english/typify


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The specification of Exchange Requirements represents the last step of the overall project setup and is the

basis for all later quality check and model validation services (see Section 7.6). Therefore, Exchange

Requirements are specified for each domain as shown on the example in Figure 15.

Once the domain-specific ERs and the check rules have been defined by a BIM expert, the end users (designers)

can finish the ER setup for their project. As shown in Figure 16, a user can then (1) Load Selected Tasks from the

preceding “Design process setup”, (2) Select for each task a related domain-specific ER that shall be checked with

regard to the future eeBIM model, and (3) Load the corresponding check rules, so that during the design

workflow all rules are ready for use for the ER check which are performed on demand.

Figure 15: Exchange Requirement Table for the architectural domain

Figure 16: 3-Step Exchange Requirement Setup


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7.2. Design Tools

Design tools integrated in the eeEmbedded platform are on the user layer of the Virtual Lab. They are either

off-the-shelve CAD systems like Autodesk’s Revit®, or software products of the consortium partners that

have been extended or developed during the project (DDS-CAD - for HVAC Design, and eeBACS Wizard – new

tool developed for BACS design). Their purpose in the eeEmbedded methodology is to provide the initial

design input for analysis at the respective steps of the urban, early and detail design phases.

7.2.1. Architectural Design

Architectural design is accomplished by using a CAD tool that is certified to export standard BIM-IFC

files. In eeEmbedded predominantly Revit® has been used but Nemetschek’s ALLPLAN® is another possible

option. The emphasis of the project in that regard has been on the specific modelling and exchange

requirements that need to be fulfilled to ensure correct energy and LCC analyses.

Architecture Modelling and Exchange Requirements

Ensuring the quality of the model is essential for a streamlined process and the delivery of the

schedule analysis services and design steps. The most important factor for modelling is to use software

which supports the open IFC standard and enables tool-neutral information validation. The IFC model is one

of the main sources for analysing each step in the overall design workflow and it allows transferring different

exchange information within and between the different design domains. The most important model

investigations are as follows:

IFC Object Structure

The structure shown on Figure 17 needs to be observed for each IFC model as an input for all

subsequent information exchanges and mappings to/from design, analysis and simulation services and tools.

Figure 17: Principal IFC model structure on the example of the W2 pilot


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The IFC Model includes project, site, topography, surrounding neighbourhood buildings, storeys, spaces and

construction elements. The latter comprise walls, windows, doors, slabs, roofs etc. Terrain geometry and mass

models of neighbourhood buildings are modelled as different object but connected to IfcSite. Normally, IfcSite

means the construction site in closed sense. However, tor energy-related analysis the geometry of

neighbourhood buildings should be available within the IFC model as mass models whereas the neighbourhood

buildings are normally located outside the construction site in the closed project-specific sense.

Exchange information

This comprises:

Project Information, such as postal address of the site, project attributes (ID, name etc.), units of

measure, global measures (length, area etc.), decomposition type

Site Information, including site identification, spatial composition, spatial containment and various

site-related quantities. If the existing terrain contains several types of land use, e.g. parking areas,

streets, walkways etc., each of these types of land use should be modelled and named separately as

special types of terrain.

Building Information, including the building identification (ID, name, description etc.), various building

quantities, spatial composition and envelope geometry. Minimum one building per site has to be

defined with its reference point, orientation and building classification according to the applicable

national building codes. This data should be contained in instances of IfcClassificationReference,

Pset_BuildingCommon and Pset_BuildingUse in the BIM-IFC model.

Building storeys, including storey identification, various quantities and the spatial composition of each

storey.

Space related information including the space name, the space usage, specific space-related attributes

and quantities, the space occupancy and the geometric second level space boundaries defining the

complete physical or virtual delimiters of each space via instances of the IfcRelSpaceBoundary2ndLevel

relationship class in IFC.

Exporting IFC with Revit Plugin

For easier IFC export, a Revit plug-in called OneClickRevitToIFCExport has been developed by STRABAG.

All relevant settings are configured in advance within IFC export settings as shown in Figure 18.

Figure 18: IFC 4 Coordination View 2.0 for IFC4 export

The user runs the plug-in with a single click, which provides a comfortable and time-saving approach for

creating numerous IFC models with the same export settings. This solution minimizes errors which users may

make during the export configuration.


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Figure 19: Revit IFC export plug-in developed by STRABAG

Quality Checks

For the validation and quality check of IFC the following software tools can be used:

EDMmodelChecker/EDMstandAlone – stand-alone command line tool of partner EPM which validates

a model according to the IFC schema and the exchange requirements

FZK Viewer 4.7 (KIT) – powerful IFC viewer which incorporates model validation functions according to

the model schema

iTWO BIM Qualifier – validates the model specifically for iTWO requirements

Solibri Model Checker – established legacy tool for code checking of the model for different design

domains (architecture, fire safety, fire escapes etc.)

The ER and KP checking and model validation services developed in eeEmbedded (see Section 7.6).

Figure 20: Screenshots from the validation of the IFC model of the Z3 pilot building using the FZK Viewer 4.7 (left)

Challenges in generating an IFC file

Generating an IFC file that fits for all the software applications is not a straightforward task. Despite

following the outlined modelling and exchange requirements and recommendations there are still a number

of challenging issues regarding the correct generation of IFC data for the purpose of energy-related analyses

and simulations, which are not yet fulfilled by legacy authoring tools. They are discussed in the project

deliverable report D9.4 regarding the validation of the eeEmbedded platform on the example and

experience obtained from the performed tests and pilot studies (Sprenger & Poloczek, 2017b). Furthermore,

the possible specifications in the export functions of the legal CAD systems (even if they would work) are not

sufficient for some of the IFC contents. The MVD approach of buildingSMART does not yet solve this issue.


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7.2.2. HVAC Design

HVAC design software (DDS-CAD) facilitates complete design and calculation of pipe and duct systems.

Based on the intelligence information extracted from the architectural BIM-IFC, integrated calculations like

air flow requirements, heat/cooling loads etc. enable developing optimal HVAC systems for the building and

its usage. With DDS-CAD’s openBIM commitment, all types of available open construction library elements

can be used, even though the components must be enriched with technical data.

The role of HVAC design software in the early design stage is to import and analyse Space/Zone client

requirements like occupancy, schedules, room temperature, activity, level of comfort etc. in addition to the

spatial variants like area/volume, shape, location, glazing properties, sun blinds and so on. When these

requirements are collected, and the external climatic conditions such as winter/summer temperature,

humidity, solar radiation (ESIM) etc. are taken into account, a set of design criteria is applied to build up one

or several variants of the HVAC systems. The different variants provide for comparing options with regard to

to different building cost, physical space requirements, operating expenses and so on. All HVAC system

variants will have enough information in itself to transfer operational demands to BACS.

It is expected to read all Space/Zone requirements, except from the external climatic conditions, from an

architectural BIM-IFC model, where object properties such as Pset_SpaceThermalRequirements,

Pset_SpaceOccupancyRequrements etc. are correctly filled, and the design criteria like Pset_SpaceThermalDesign

are either provided from energy or CFD simulation tools, or by the HVAC design tool itself, which will use this data

to determine HVAC demands such as the size of air diffusers.

All variants of the HVAC model should have a minimum of information contents for the BACS to use, and all

components must be assigned to an IfcDistributionSystem with correct enumerated type. Components like

Air Terminal, Damper, Air Handling Unit (AHU) etc., where there is a direct link to the BACS software must

obligatorily contain this information.

Using the knowledge obtained from the HVAC design allows meeting TO-BE KPIs and KDPs quicker and

better. DDS-CAD’s approach to this information flow is to use standardized „openBIM“ knowledge templates

to all disciplines.

Figure 21: Example of the pre-dimensioning and modelling of one HVAC alternative with floor

heating (DDS Tool)

Figure 22: Example of the visualization of one alternative for ventilation with AHUs and ceiling induction in the Multimodel

Navigator of eeEmbedded (see Section 7.4)


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7.2.3. BACS Design

Including building automation and control systems (BACS) design into the holistic eeEmbedded

methodology is a new finding of the project which goes clearly beyond the current state-of-the-art process.

It has been figured out that the energy efficiency

classes according to EN15232 provide a suitable

guideline for such a design because this beings

different equipment such as AHUs, heating units

etc. down to a formal, computer-interpretable

categorisation.

In order to perform the BACS design, several underlying steps have to be performed in advance:

Proper IFC based BIM model of all spaces/zones of interest

Definition of functional relationships within the HVAC design as well as the usage of proper IFC standard

functional descriptions (such as IfcValve, IfcBoiler etc.)

The first and most important added value by using this approach is that energy efficient solutions are

designed in an early phase based on the provided models. In addition, the developed solutions are

comparable with previous projects and templates can be reused in other projects.

Figure 23 shows a screenshot of the developed eeBACS Wizard of the eeEmbedded project which supports

designing BACS consistently using BIM-IFC data and interacting with architectural and HVAC design using the

shared model data and the collaboration and resource management tools of the eeEmbedded platform. The

beta version of the eeBACS Wizard developed during the project supports the user in creating energy

efficient solutions from the beginning. It starts by analysing the available HVAC model and suggests a

minimal setup of control strategies based on the desired energy efficiency class according to EN15232. The

user can then customize the solution according to additional key points. The tool provides templates for

products including costs and approximated energy usage over the life time.

Figure 23: Screenshot of the developed eeBACS Wizard


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7.2.4. Energy System Modelling (ESIM)

Especially within the Urban Design stage architects and clients are interested in elaborating and

comparing different system approaches of energy system concepts on a general systemic level. The analysis

of current design steps involved in the creation of an energy system showed the informational gaps and the

need for a unified description of energy systems. In that regard, the identification of core parts within the

energy system in combination with a cross-domain-oriented set of properties was the starting point for a

concept targeting the development of a unified modelling approach.

Systems Engineering with its related modelling languages UML and SysML is the approach used for the

description of energy systems. A wide range of standards are available or currently under construction for

this topic. The achieved project results have to be interpreted as a snapshot of the current cross-domain-

driven movement towards the formalization of a common energy system description.

Figure 24: ESIM core parts and link model approach connecting related elementary models

Based on the analysis of existing approaches, a formal specification and conceptualization of the ESIM

ontology was achieved. The development of that ontology comprised five main steps, namely (1) require-

ment analysis, (2) vocabulary definition, (3) ontology conceptualization, (4) ontology search, selection and

reuse, and (5) ontology implementation.

Modelling environments supporting the system and requirement engineering approach by using the System

Modelling Language (SysML) are predestined to support a formalized system-oriented modeling approach.

In eeEmbedded, the need of different services for ESIM on knowledge level was clearly defined and the

functional requirements and intention of a set of related services were identified. This includes: (1) Converter

services, (2) Filter and query services, (3) Simulation mapper services as well as (4) Template management

services.


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Currently experts of several different domains such as ‘Smart City’, ‘Smart Grid’, ‘Smart Home’, BIM and

others are working intensively on the standardized modelling of several parts of the energy system as well as

the more formulized description of buildings and their neighbourhood. Within this topic a highly dynamic

development is recognisable resulting in completely new or re-worked standards and ‘best practice’

guidelines with high impact on the introduced approach of ESIM. Therefore, all findings regarding this topic

have to be understood as a starting point. The work on ESIM - especially the implementation - is an ongoing

task which goes beyond the timeframe of the eeEmbedded project while considering actual developments

within the external standardization activities.

7.3. Scenario Manager

The Scenario Manager (ScM) is a new product developed within eeEmbedded following the holistic design

methodology based on the IDM approach. It can support any BIM project in different design phases and it

was developed as a general tool which supports team management, process management and data

management for every project participant as mandatory part of the User Layer of every Virtual Lab

configuration (see Figure 10). Its features comprise:

Team management

o Define BIM teams

o Assign users to teams

o Define team roles for users (BIM manager, architect, energy expert, …)

Process management

o Define workflows for different design phases (urban design, early design, detailed design)

o Create tasks, gateways and flows following the BPMN standard

o Specify control points and design loops

o Assign users to tasks

o Assign exchange requirements (ERs) to tasks

o Issue notifications when users have to work on tasks

Data management

o Setup requirements for information levels, define target values and select possible building entities

o Share files with other users using Multimodel Containers and BCF topics

o Check data quality based on ERs

o Automatically upload/download files to/from servers on the cloud

o Merge data from different sources

o Help to prepare data for analyses

o Help to prepare results for KPI visualization.

Figure 25 shows the communication of multiple Scenario Managers within the eeEmbedded platform

(ScM A & ScM B). The applications are connected with the central BCF server, BIM—it (see Section 7.5.1) and

both are using the same workflow management engine (WFM Engine). This engine is responsible to load


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process models (BPMN diagrams) and to verify the status of the current running process. It notifies the

Scenario Managers about changes in the workflow, for example when a task was completed or a problem like

failed ER checks or failed key design parameter checks occur. Users can upload any file to the workspace and

can download all files from other users where they have access rights. The workspaces are synchronized each

time a handover to the next user in the workflow is done. The data which will be synchronized is held in

Multimodel Containers (see Section 6.3). A receiving application of a Multimodel Container selects the domain

models which are needed in its workspace and has to look into the Link Models to retrieve the information. The

Multimodel Container is attached as BIM snippet to the communicated BCF topic. This is an enhanced

approach for BIM—it (Section 7.5.1) which previously was focusing only on IFC data.

Figure 25: Communication between multiple ScMs on the eeEmbedded Virtual Lab platform

A BPMN editor included in the ScM supports the major BPMN elements like user task, flow, exclusive and

parallel gateway, start event and end event. The project manager models the workflow and uses tasks which

are specified in the key point setup table. S/he can assign to every task one user and some ERs. The ScM

emphasizes in every task which actions are supported in that workflow stage. Figure 26 shows the task level

view. User actions which are currently not possible are greyed out. Possible user actions are in bold black

colour. Each button is connected to applications like the EDM model server, the KPA tool or the Multimodel

Navigator. The data is transferred to and from those tools automatically. Hence, the user does not have to care

about how to store the data. Task files can be imported in the ScM via Drag&Drop. Once the user has

completed a task he can finish it and the handover to the next user will be done automatically as defined in the

BPMN diagram.


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Figure 26: Task level view in the ScM with emphasized user task (red colour) and possible user actions

7.4. Multimodel Navigator

The Multimodel Navigator (MMNav) is the second mandatory tool in each Virtual Lab configuration on the

User Layer of the eeEmbedded platform. It enables the user to check key design results arising in the various

domains of the design process. It provides also advanced functionality to set up new construction conditions

and to execute various simulations of proposed design variations and alternatives by the design team.

The Multimodel Navigator was developed on top of the bim+ platform of Nemetschek. It is a multi-media-

based navigation and visualization application that allows users combination of different graphical

representations of results elaborated in the eeEmbedded design scenarios. The tool runs on standard

Internet browsers and does not require software installation. For this reason, the MMNav is available

everywhere and on any device, which could be decisive for decision makers. The following Figure 27

provides a graphical overview of the most relevant functional units of the MMNav.

The application features:

Identification of graphical and numerical deviations, such as comparison of pre-set TO-BE design values

with actual AS-IS Values

Assignment of new construction templates, space use templates, or other numerical settings such as

climate data, latitude or longitude, north direction etc.; this allows users to run simulations as early as

possible and to examine different materials and construction variants

Ordering the execution of different simulations under variant conditions - Energy, Wind, Life cycle costs etc.

Storing design decisions and corresponding construction conditions

Support of communication issues.


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Figure 27: Functional Units of the MMNav (blue: developed or enhanced by eeE)

The following figures present some of the main features of the MMNav developed during the eeEmbedded

project. Figure 28 shows the use of the MMNav during the verification of Key Design Parameters (KDP). The user

is able to control the KDPs by opening and activating the nodes of the break-down structure. Each KDP is assigned

a traffic-light colour which indicates the deviation of the AS-IS to TO-BE Value (fulfilled, critical, not fulfilled). In the

presented example, all outer walls, which do not fulfil the Window-Wall-Ratio, are shown in red.

Figure 28: Verification of the Key Design Parameter “Window Wall Ratio (WWR)”.

3. ADMINISTERING PROJECT MEMBERS

Inviting persons to projects

Managing member roles

2. ADMINISTRATION OF BIM MODELS

Import of models via web service

Creating model revisions

Filtering of models

5. ADMINISTRATION OF PROPERTIES

Definition of project/model/objects

properties sets

4. PROJECT ATTACHMENTS

Simulation attachments

Filtering of attachments

Attachment of properties to BIM objects

1. ADMINISTRATION OF PROJECTS

Project views

Project selection

Filtering of projects

Multimodel Navigator’s functional Units

6. MMNav VIEW CONTROL OPTIONS

Basic Visualization options

Federated Model views

Object Explorer & Filtering by attributes

Information detail panel and attachments

Visualization of KPIs and KDPs

Definition of Multi-Model Structures

Definition of construction variants

Collaboration in eeE Scenarios

Visualization of Simulation results


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Figure 29 shows the construction templates break-down structure (left menu), which allows users to define

different Construction Variants. The assignment of objects to specific construction templates is supported by the

menu on the right. The navigation window is highlighting the walls in different colours, which corresponds to

individually selected construction templates. The linking of objects to construction templates is stored on the

cloud and can be easily recalled by the MMNav with the help of the Scenario Manager.

Figure 29: Assignment of objects having the same construction type for a given design variant

Figure 30 presents an example of the visualization of simulation results. Simulation results are automatically

attached to respective individual objects via the ScM after the simulation service is run. These results are

accessible for the user by clicking on the attachments in the menu on the right side.

Figure 30: Room simulation results attached to the related space object


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7.5. Collaboration and Resource Management Services and Tools

Collaboration and resource management services and tools are in the core of the Virtual Lab environment.

They are responsible for the common use of the eeEmbedded service bus (Communication Layer) and for the

intelligent access to the Repository Layer (Storage Cloud) and the analysis and simulation services (Compute

Cloud). In the current eeEmbedded platform realisation these services include BIM–It, supporting the BCF-

based communication, the EDM Model Server, providing a number of BIM-related data management

services, and a set of Cloud Services facilitating the parallel analysis of multiple design variants.

7.5.1. BIM—It

IABI’s BIM--It web application is a modern collaboration platform built for easy interoperability and

exchange between partners in all project types of the AEC industry. While it is fully useable with any modern

web browser, BIM--It is also built with an accessible API to allow the integration of external tools. In

eeEmbedded, the Scenario Manager utilizes the developed BCF API to connect multiple platform services

with BIM—It (see Figure 25).

The platform was originally developed to be a BCF capable collaboration server but has since evolved into a

tool that also offers services for project management and model analysis. It was the first available product to

support the BCF API, shortly after it has been officially released by buildingSMART International in early

2015. Multiple software companies use BIM--It as a reference implementation for a BCF Server and have

developed test suites against the platform.

BIM--It is extensively described in the eeEmbedded Deliverable D8.2. Its main use case is to coordinate

multiple project participants and to visualize building models for further checking and analysis. As a web

application, BIM--It is hosted in the Microsoft Azure Cloud. It’s an Asp.Net Core web application backed by a

relational SQL Server database. Some compute-intensive functionalities, such as converting building models,

is outsourced to a micro service architecture accessed via http.

Figure 31: Detailed view of a single issue in BIM--it

https://www.bim--it.net/


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7.5.2. EDM Model Server

The EDMmodelServer (EDM) provides a full featured repository for storing and manipulating ISO

10303 STEP EXPRESS based data, of which IFC 2x3 and IFC4 are two implementations. A fully featured EDM

server provides services adapted to both thin clients like web browsers and smart phones, and fat clients like

CAD systems and other full IFC data handling clients.

Opposed to the more common file based storage for IFC BIM data, EDM stores data as models, where each

model can logically refer to other models. While a model can be exported and imported in its entirety as a file, it is

often necessary to export and import subsets of a model with operations like extraction and merging. The

database system is designed to hold any number of models and each model has no size or instance number limit.

EDM can also store as part of the model data files (BLOBs) in the same way as any other attribute.

Supported file and transfer formats

The EDM can natively import and export data in several formats, including:

ISO10303 Part 21 Step Physical File Format (SPFF) and Part 28 XML format (P28)

IfcXml, an implementation of P28 XML

For Web Services: SOAP xml (Simple Object Access Protocol) and JSON (JavaScript Object Notation).

EDM supports any data defined by ISO10303 Part 11 EXPRESS Schema (P11) on full semantic level.

Applications, services and utilities are available for several important industrial standards like ISO 12006-3

(used for BuildingSMART Data Dictionary bSDD) , IFC2x3 and IFC4. As long as data is defined according to a

P11 Schema, import and export of this data are automatic processes. All data can be sent / received in the

P21 and P28 (XML) format. In addition, any other data formats like e.g. Comma Separated Values (CSV) can

also be accepted, but will require mapping. Traditionally, defining schema for and interfacing with such

legacy data is called „adaptation” and is done by implementation of „adapters“. For simpler data structures,

setting up an EXPRESS schema and adapters for the data is usually a relatively simple task.

Basic Functionality

EDM supports validation and integrity checks against the employed EXPRESS schema, in case of

eeEmbedded IFC4. The function layer checks automatically all data that are imported to the system against

constraints defined by the target schema. Express-X can be used to add user-defined rules for additional checking.

Merge / extract are powerful options for adding, replacing and extracting subsets of data, for example

domain models (HVAC, BACS, EL) in an IFC file, or embedded “non-traditional” data like lease areas or

maintenance data.

Extended Functionality

In those cases where the XPX language is not sufficient for implementing the requested functionality,

EDM supports the use of Server Side Plug-ins (SSPs) written in a generic programming language like C# or

VB.NET. A typical scenario for use of SSPs occurs when data and services provided by other nodes need to

be accessed, for example when:

Data validation requires the server to read ontology data from an external source

A workflow requires the server to send a mail to a particular user.

Such functionality can be implemented using SSPs. The effect is that the EDMmodelServer will both act as a

service provider and a service consumer on the service bus.


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Accessing server-side functionality in the EDMmodelServer

There are two main approaches for accessing data and functions in the EDMmodelServer:

Using an API, utilizing a binary protocol between client and EDM. This implies that the client links

to an EDM library for accessing the functions. The API functions hide the data transfer layer

between client and server. APIs called language bindings are available for several programming

languages like C# and Java.

Using a Web Service interface like http/SOAP or http/REST, utilizing a general “text based”

protocol between client and EDM. In this case there is no direct software dependency between

client and server. Access is provided via XPX queries. This means that as soon as a database query

is defined using XPX, it is also available on the web, provided that the client has the necessary

trust level. This mode is well adapted to “thin clients” like web browser applications.

Management of the EDMmodelServer-ifc and its data

As a companion to the EDMmodelServer, EPM delivers also the EDMmodelServerManager (MSM),

which provides access and user interface to a vast set of functions like

Model management (import and export, merge, extract, versioning etc.)

User and access management

Defining and running reports

Browsing and manipulating models (2D and 3D graphical viewer, user definable tree breakdowns,

property filtering and editing and much more).

Typical applications of EDM in the AEC industry are shown in Figure 32 below.

Figure 32: EDM Server Applications


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7.5.3. Cloud Services

Calculations involved in energy simulations demand large computing resources and thus deployment in the

cloud is an inevitable prerequisite for efficient exploration of design options towards achievement of optimal

building performance. Development work on cloud services exploited prior availability of all analysis tools as cloud

enabled applications and delivered a cloud framework abstraction that provides plug-ins for major existing cloud

frameworks and can easily cope with future cloud frameworks with minimal developer effort. The resulting cloud

services comprise: (1) a Broker Service that translates requests from the eeEmbedded service bus to various

cloud frameworks, and (2) Plug-ins to implement different cloud engine interfaces.

Figure 33: The eeE Cloud Framework

The Cloud Broker Service encapsulates all the details of the underlying cloud infrastructure masking

differences and implementation details (like hosting server location or specific cloud system details) thereby

providing a uniform eeEmbedded Cloud API. To guarantee that the Cloud Broker Service is globally accessible,

in the eeEmbedded service bus an advertisement/registration mechanism is used to publish service availability.

In particular, the Broker Service has been used to perform energy simulations with TRNSYS (Section 7.7.2) and

CFD simulations with the 3D Wind and 3D Therm applications (Sections 7.7.3, 7.7.4) using the developed API.

Figure 34: Energy Analysis running on a private cloud


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Figure 35: CFD Simulation Running on the HPC cloud system offered by a European Research Institute

The use of cloud based computing resources enables the management of simulations running in parallel.

However, this leads also to certain requirements regarding the simulation management concept which are

an inherent part of the overall simulation and optimization framework.

The simulation management is enabled by a Simulation Model Mapper which is part of the analysis domain

reflecting special needs of the used analysis tools as well as the used cloud environment.

The required capabilities can

be differentiated into (1) non-

functional, (2) functional and

(3) workflow-related.

Non-functional capabilities

are scalability, maintaina-

bility and adaptability

Functional capabilities are

related to infrastructure and

access management, self-

management or health-

control and information

acquisition and delivery

Workflow-related capabi-

lities are input management,

job-analysis-tracking, output

management and, as optio-

nal part, data archiving.

Figure 36: Example showing the information flow


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7.6. Model Validation Services

The model validation services are another important part of the core layer of the Virtual Lab environment.

While not mandatory for the functioning of the overall system, they are crucial for the achievement of

efficient high quality design because they ensure that modelling errors and gaps are largely avoided by the

exchange of model data within the design team, requirements are met and key points are appropriately

observed or deviations identified as early as possible. Therefore, the use of these services are highly

recommended by all end user partners in the eeEmbedded consortium.

7.6.1. Ontology-based Exchange Requirement Checking and Visualisation

The ontology-based Exchange Requirement Checking enables capturing the information exchanges

according to the buildingSMART Information Delivery Manual (IDM) as well as steering and tracking them

during the design process. It helps to systematize design tasks, level of information agreements and

exchange requirements (ER) for the various BIM uses/phases and configure them for each specific project in

a digital environment.

Implemented as a service the ontology-based ER Checking is available via the Scenario Manager (ScM). It

provides the functionality to setup and run ontology-based exchange requirements verification and

visualization. The added value is in insuring the model quality and the compliance with the information

requirements of the successor in the design process.

The overall ER Checking approach is outlined on Figure 37 below. It comprises the following steps:

(1) Exchange Requirement Setup: Exchange Requirement Specification as part of the Project Setup

(2) Exchange Requirement Setup: Defining Multi-Model View Definitions regarding the exchange

requirements by implementing ontology-based ER Checks using SPARQL queries

(3) Exchange Requirement Checking: Preparing the ER Checking by selecting relevant data models (e.g. IFC

and ESIM) as input data

(4) Exchange Requirement Checking: Performing the ER Checking on the basis of the input data

(5) Exchange Requirement Visualization: Generating a validation report for the end-user and result file for

the following Key Point Checking.

Figure 37: Overview of the ontology-based ER Checking


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Exchange Requirement Setup (Steps 1 & 2)

The specification of the Exchange Requirements and the corresponding mapping to a specific data schema is

part of the overall project setup (see Section 7.1). The setup of the ontology-based Exchange Requirement

Checking is also part of the project setup. However, in order to prepare for later ER checks some additional

preparation work is necessary. The tables used for detailed ER specification shown in Section 7.1 are thereby

extended by an additional column providing the links to the ER check definitions, which are specified

separately in advance using SPARQL queries. Figure 38 shows as example the SPARQL query to check if an

IFC model contains an IfcSite object which is decomposed by at least one IfcBuilding object.

Figure 38: Definition of an ER check based on the data schema mapping using SPARQL

Exchange Requirement Checking (Steps 3 & 4)

In order to warrant the quality of the data models of a design alternative within the eeEmbedded design

process, an iterative process of model creation (authoring tool) and ontology-based ER checking has to be

performed. After each design task (e.g. Create Design Cubature Options) the ER checking service can be

invoked through the ScM. By clicking the ER/LOD check button the task-related ER table including ontology-

based ER check is started and validates the quality of the elaborated design (see Figure 39).

Figure 39: Screenshot of the Exchange Requirement Checking Service for ER-1.10 ARCH


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Exchange Requirement Visualization (Step 5)

After the ER check has validated the data model input the result file highlights all information gaps in the

provided design alternative. Depending on the Exchange Requirement specification the ER check validates a

single data source or a multi-model. In the following Figure 40 the result of the ER check is visualized for the

Exchange Requirements that specify the needed information from architectural design. The colours in the

first column of the ER specification indicate whether information is available (green), not present (red) or

calculated/derived from other information (orange).

Figure 40: Visualization of the ER Checking results indicates in the first column whether the required information is available (green), not present (red) or calculated/derived from other information (orange).

7.6.2. Ontology-based Key Point Checking and Visualisation

While ER Checking is responsible for the quality of the input models, the KDP Check aims to check the

quality of the results of a certain design task. The requirements for KDP checking are defined during the

project set up phase (see Section 7.1). The KDPs can thereby be interpreted as extension of the ERs: ERs

define that an entity and respective entity attribute(s) should exist in the input model, while KDPs define the

ranges in which the attribute values are valid.

Before the KDP check can be triggered, similar to the ER Checking, the Multimodel Container and its content

has to be transferred to the KP ontology. This transformation is realized in several steps. In the first step the

relevant element types are transformed into OWL. This is realized by transforming the types from the Excel

sheets into SPARUL as an intermediate step. The SPARUL code is then interpreted by the Ontology

Verification Service (OVS) and the types are then mapped into OWL and linked with the predefined Link


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Ontology. After this step, the predefined Ontology Schemas of the KP ontology and the IFC ontology are

linked together with the types. The result is a linked schema of KPs, types and IFC elements.

Figure 41: OWL Representation of the Multimodel

After the schemata are created in OWL, the instances are generated. The target values of the KPs and the

types are defined in the project setup (see Section 7.1) where the specific links between KPs and types are

defined. Similar to the schema generation, instance generation also uses SPARUL code. For the comparison

of the set TO-BE and the actual AS-IS Key Point values the IFC model is transformed into IfcOWL using the

mapping tool from buildingSMART (http:// http://ifcowl.openbimstandards.org/IFC4_ADD1/index.html). The

final result is a complete ontology consisting of KP, type and IFC schemata and instances. Figure 42 shows

how the process is prompted within the project setup by a corresponding Excel-based GUI.

Figure 42: Multimodel generation in the project setup phase via the Excel-based GUI

TARGET VALUES ASIS VALUES

TYPE

ONTOLOGY

SCHEMA

IFC OWLSCHEMA

TYPE

ONTOLOGY

INSTANCES

KPONTOLOGY

SCHEMA

IFC OWLINSTANCES

KPONTOLOGY

INSTANCES

Link Ontology

KPValues

SelectedTypes

IFC Step Model

SPA

RQ

LR

ule

s

On

tolo

gy L

eve

l D

ata

Leve

l

Configurations for Model Generation

C:\Users\makad\Desktop\20170717_ScM_Project-Setup_MASTER-file_v24C:\Users\makad\Desktop\20170717_ScM_Project-Setup_MASTER-file_v24

Multi Model Selection

Select Multi Model C:\Users\makad\Desktop\20170502_eeE_Multimodel\20170502_eeEC:\Users\makad\Desktop\20170502_eeE_Multimodel\20170502_eeE

Select Variant

Start Model Creation

OWL Model Generation Parameters

SPARUL for Generate Entity Types

SPARUL for Entity Types to IFC Schema

OWL for KP Ontology

SPARUL for KPs to Entity Types

Yes

Yes

Yes

Yes

SPARUL for Entity Types to IFC

OWL for IFC Yes

Yes

Workspace Folder

http://ifcowl.openbimstandards.org/IFC4_ADD1/index.html


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Both the TO-BE model and the AS-IS model are encoded in OWL. To check for the correct results, the

relationship between both models is described via SPARUL. The checking can then be triggered by the

Ontology Verification Service. In order to start the KP checking via the ScM the different SPARUL rules have to

be related to the different tasks. This information is included in the KP spreadsheets. Figure 43 shows an

example for the task/KP relation.

Figure 43: KP – Task Relation

The KP checking itself produces tabular results in a similar manner as the ER check service. Additionally, the

results can be visualised in the MMNav via automatically generated colour codes, which show the level of

fulfilment of a selected design parameter for all BIM elements associated to it. This is a very useful feature

since it allows quickly identifying gaps and critical points in the design. An example is shown in Section 7.4

which outlines the features of the MMNav (cf. Figure 28).

7.7. Energy Simulation and Analysis Services and Tools

Energy simulation and analysis applications are required to properly assess energy performance and enable

objective evaluation of various design variants. In the scope of eeEmbedded three such applications have

been integrated: (1) TRNSYS, providing for comprehensive thermal analyses/simulations in all targeted

design phases, (2) 3D Wind, providing for simulation of wind influence to assess building location and

orientation with regard to the neighbourhood and verify key parameters already in the urban design phase,

and (3) 3D Therm, providing for deep CFD simulation of selected critical zones/spaces to evaluate thermal

comfort in the detailed design phase.

7.7.1. Input Preparation

Typically, energy simulation analysis tools imply sophisticated mathematical models and therefore

require complex preparation of the input models, which is mostly done manually by an energy expert to

ensure that the simulation model reflects the design intent correctly. However, as energy performance is

affected by a high number of design parameters, whose influence is not easy to predict, today’s design

process calculate only a very few variants. This often leads to suboptimal solutions.

The eeEmbedded methodology suggests an approach to improve that situation by re-engineering the

simulation and analysis tools and thereby splitting the pre-processing, analysis/simulation and post-

processing steps into separate processes. This enables preparing a large number of parameter variations by

the end users (architect, HVAC designer or energy expert), which are then executed in parallel on a compute

cloud and compared and evaluated on the basis of the computed KPIs by a specialised Multi KPA Tool.

KP ID KP type KP group KP sub-category Name Task-ID Task-NameKDP01 KDP design geometry Building Height 20-10 20 01 Create design cubature options

KDP02 KDP design geometry Floor to floor height groundfloor 20-10 20 01 Create design cubature options

KDP03 KDP design geometry Number of floors 20-10 20 01 Create design cubature options

KDP04 KDP design geometry Gross floor area (GFA) 20-10 20 01 Create design cubature options

KDP07 KDP design energy U-Value Wall 20-10 40 01 Create energy system concepts

KDP08 KDP design energy U-Value Base Slab 20-10 40 01 Create energy system concepts

KDP09 KDP design energy U-Value Roof 20-10 40 01 Create energy system concepts

KDP12 KDP design energy Air tightness 20-10 40 01 Create energy system concepts

KDP13 KDP design comfort temperatures winter (perimeter) 20-10 40 01 Create energy system concepts

KDP14 KDP design comfort design temperatures winter (core) 20-10 40 01 Create energy system concepts

KDP15 KDP design comfort design temperature summer 20-10 40 01 Create energy system concepts

KDP16 KDP design geometry Window Wall Ratio (WWR) 20-10 40 01 Create energy system concepts

KDP18 KDP design energy systems Energy system cooling 20-10 40 01 Create energy system concepts

KDP19 KDP design daylight Area of regularly occupied spaces within 7.5m of exterior windows or atrium 20-10 40 01 Create energy system concepts

(1) Load KDPs and selected tasks (2) Setup KDP Check


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Crucial in that regard is the achievement of a high degree of automation in the input preparation. This

involves the following steps:

(1) Semi-automated assignment of design variants in the MMNav

(2) Automated generation of stochastic parameter variations to enable optional uncertainty analysis in

order to better estimate lifecycle risks

(3) Automatic generation of a Variation Model, linking the defined parameter variations to the base BIM-

IFC design model

(4) Using BIM2SIM plug-ins for the simulation tools to perform the transformation of the data from the

BIM model to the analytical simulation input models; here the most difficult part is the proper

interpretation of the geometry and the topology of the building and the respective generation of 2nd

level space boundaries, which are needed for the thermal simulations

(5) Using the developed cloud API to provide for fast parallel computation of all generated variants.

The first two of these steps are performed by tools embedded in the ScM on the User Layer, whereas the

subsequent three are performed automatically with the help of the support services on the Core and the

Analysis Virtual Lab layers.

In Step (1) design variants are created on BIM level using the MMNav. The process is facilitated by the use of

a library of templates for various climate and occupancy profiles as well as various element construction

types (for walls, slabs, roofs, ground plates, windows, doors) and the associated materials. Essential in that

regard is the association of element types to standard OmniClass items which can be done in the project

setup phase and enables initial fully automatic template assignment (see Figure 44).

Figure 44: Automatic element-template assignment in the Multimodel Navigator

Using templates, parameter variations can be easily done in the MMNav user interface, shown schematically

in the following Figure 45. Creating a design variant only requires the input of the desired parameter


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changes, whereas all other data is automatically interlinked to the new variant from the imported base BIM-

IFC model.

Figure 45: The user interface for template assignment in the Multimodel Navigator

In Step (2), performed in the case of a desired Uncertainty Analysis, a special input preparation tool called

Sampling Service is additionally used. It can be launched from the ScM. However, before the sampling can be

performed, some parameters have to be configured. These are the chosen number of samples, the time

interval for the output time series, the building location and the number of days each sample should

comprise (see Figure 46).

Figure 46: Sampling Service GUI

When the sampling service is performed, it produces required occupancy and/or climate samples and saves the

generated data in the multi-model. In addition, the Link Model is updated with the new stochastic variants.


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Thus, not only architectural and engineering design variants can be examined but also parameter ranges,

reflecting the uncertainty of the design values at a certain project stage can be taken in consideration to enable

better informed design decisions and better understanding of the designed building’s performance.

7.7.2. Thermal Simulations with TRNSYS

Energy analysis is included in each part of the design process. Following the intended overall

approach, sophisticated analysis tools modelling the thermal as well as operational behaviour of the building

as well as main components of the energy systems need to be integrated into the workflow to support the

decision-making process. In eeEmbedded, the software system TRNSYS-TUD is used for this kind of analysis.

TRNSYS is the abbreviation for TRaNsient SYstem Simulation program which was initially developed by the

University of Wisconsin in Madison, USA before approximately 35 years. The software is on sale as

commercial software package targeting energy experts. It is continuously updated whereby parts of the

software code are distributed in source form but the main core parts are available only as binaries.

The project partner TUD-IET started the development of its own individual software modules for TRNSYS in 1994.

Since this time more and more new simulation modules were added or existing modules were re-shaped

respectively adapted to support the special tasks within research projects. Because of the high rate of newly

developed software modules and approaches which are unique compared to the software setup which is

available as commercial solution, the suffix TUD was added to the original trademark TRNSYS to indicate the

difference between the commercial tool and the research-orientated solution TRNSYS-TUD. The additional or

modified software modules developed by TUD-IET are not available for purchase or free distribution.

The simulation package in eeEmbedded is adapted and configured for modelling energy systems and related

buildings. It is based on a modularised concept which provides a high level of flexibility when analysing

various kinds of energy systems while involving peripheral conditions and related phenomena. Because of

the modularised approach of the software design experienced users have the possibility to add their own

software code to the core software parts to process simulations according to their individual tasks.

Overall role of TRNSYS-TUD

In the eeEmbedded context TRNSYS-TUD acts as a common placeholder for similar software systems

used for transient thermal building and energy system simulation. In a productive environment, a

comparable tool with similar functionality such as EnergyPlus could be used too.

Figure 47: Workflow within the energy analysis domain, embedding the simulation tool into the Analysis Framework


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In general, the integration of the sophisticated analysis task on the eeEmbedded platform can be divided

into three main sub-steps: (1) pre-processing, (2) analysis processing and (3) post-processing (see Figure 47

above). TRNSYS-TUD covers components which are designed for pre-processing purposes, e.g. to transfer the

building model provided by the architectural domain into a simulation model covering the building

geometry, energy system components (depending on the design stage) as well as user behaviour and climate

conditions. This complements the assignment of design variants in the MMNav providing for BIM2SIM model

transformation and some additional input features. On Figure 48 and Figure 49 the results of transferring the

architectural model into the geometrical part of the simulation model is presented based on the level of

detail used in the Early Design stage. In the Urban Design phase the whole analysis process covering pre-

processing, processing and post-processing is almost fully automated.

Figure 48: Pilot building STR Z3: Architectural model (IFC, left) and simulation model (TRNSYS-TUD, right)

Figure 49: Pilot building BAM W2: Architectural model (IFC, left) and simulation model (TRNSYS-TUD, right)

Interdependencies and constraints with regard to other domains

The quality of the geometrical model provided by the architectural domain plays an important role to

automatically run the analysis process as a service. The analysis software benefits from the output of the

architectural domain (Urban, Early and Detailed Design) as well as the HVAC and BACS domain (Early and


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Detailed design). Other inputs can be included as well like stochastic samples (see Section 7.7.1). If inputs are

missing, default templates or default values can be used based on client requirements or an internal

template library. However, with regard to the quality of the BIM models generated by authoring systems

there are many challenges that have yet to be overcome as outlined in the eeEmbedded validation report –

see Deliverable D9.4 (Sprenger & Poloczek, 2017).

7.7.3. 3D Wind Analysis in Urban Design

3DWind is an end-user application for the aerodynamic analysis of a building embedded in its urban

environment to obtain the best building cubature (shape and orientation) regarding wind comfort indices,

local wind potential for renewable energy use and natural ventilation design (as a result of the combination

of solar radiation and wind flow). 3DWind can be used as complementary tool to Energy Simulation

applications in the Urban Design phase.

In the context of eeEmbedded, CFD analysis utilizes IFC files for the description of the neighborhood

geometries (buildings and terrain), templates and Link Models that empower collaboration and coupling of

CFD with other simulation applications critical to Urban Design phase. Architects continuously receive

feedback from the CFD analysis and can easily make decisions about the building cubature which are critical

for all the consequent design phases.

Automations have been developed for the construction of suitable models for CFD simulations from IFC

building model of any LoD and any design phase. The translation of weather wind data to proper boundary

conditions for the atmospheric boundary layer has been developed and automated.

For the wind analysis around the building at hand, wind-rose data are used which describe the mean wind

velocities, the magnitude, the direction and the duration of the wind at a specific site within a typical year

(see Figure 50 below for the sites of the two pilot projects used in the validation phase).

W2 Pilot, Utrecht, NL. Prevailing directions SW, SSW. Prevailing wind speed 4.5m/s and significant time

intervals with 6-7m/s

Z3 Pilot, Stuttgart, DE. Prevailing directions WSW, SW. Prevailing wind speed 3.3m/s

Figure 50: Wind rose data for the two pilot demonstrators

The wind speed distribution described by wind rose refers to mean hourly wind speeds measured at 10m

above the ground. Radial distances indicate percentage of time (or exact number of hours) of wind events.

In the context of eeEmbedded, a CFD analysis is conducted at Urban Design and Detailed Design phases. At

UD phase, the CFD analysis provides wind comfort KPIs for KPA decision support process. The two predicted


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KPIs concern two representative wind velocities, the first one is the velocity at the building entrance and the

second one the velocity at city canyons around the building (Figure 51 below).

Figure 51: Wind speed at human height, resulting speeds at all points of interest organized in the form of wind rose and wind stream lines around the Z3 building (top) and around the W2 building (bottom).

According to site wind data, a number of significant directions and magnitudes of wind speeds are

considered in order to compose a simulation matrix for the CFD analysis, (see Figure 52 below).

Figure 52: Simulation matrix for prevailing wind for the Z3 building, (left) and the W2 building (right)

Simulation results are synthesized by applying frequencies (fij) of occurrence to each of the wind directions

and wind speed magnitudes, as weighting factors:

�̅�(𝑥, 𝑦, 𝑧) = ∑ ∑ 𝑉𝑖𝑗(𝑥, 𝑦, 𝑧) ∙ 𝑓𝑖𝑗

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑡 𝑤𝑖𝑛𝑑 𝑚𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒𝑠

𝑗=1

𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑤𝑖𝑛𝑑 𝑑𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛𝑠

𝑖=1

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

3.00

-3.00 -2.50 -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00 2.50 3.00

Wind Velocities 3m/sec

POINT 1

POINT 2

POINT 3

POINT 4

POINT 5

POINT 6

POINT 7

POINT 8

Rotation around Z

Wind Direction

Wind Velocities - Occurence

>60% <30% <10%

0 N 3m/s

45 NW 3m/s

67.5 WNW 3m/s

90 W 3m/s 5m/s 7m/s

112.5 WSW 3m/s 5m/s 7m/s

135 SW 3m/s 5m/s 7m/s

180 S 3m/s

202.5 SSE 3m/s

225 SE 3m/s

247.5 ESE 3m/s

270 E 3m/s

292.5 ENE 3m/s 5m/s

315 NE 3m/s

Rotation around Z

Wind Direction

Wind Velocities - Occurence

>60% <30% <10%

0 N 4.5m/s

45 NW 4.5m/s 6.5m/s

67.5 WNW 4.5m/s 6.5m/s

90 W 4.5m/s 6.5m/s

112.5 WSW 4.5m/s 6.5m/s 9.5m/s

135 SW 4.5m/s 6.5m/s 9.5m/s

157.5 SSW 4.5m/s 6.5m/s 9.5m/s

180 S 4.5m/s 6.5m/s

202.5 SSE 4.5m/s

225 SE 4.5m/s

247.5 ESE 4.5m/s

270 E 4.5m/s

292.5 ENE 4.5m/s 6.5m/s

315 NE 4.5m/s


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CFD analysis workflow

Separate IFC models for each cubature alternative and variant are received for the CFD analysis. The CFD

analysis proceeds according to the following steps:

The global site coordinates are determined by the IfcSite entity of the IFC model and the wind rose data

are consequently obtained for each location

Geometry extraction from IFC file (SOF’s application)

Model simplifications for reasonable CFD analysis (SOF’s modeler)

Imposition of CFD boundary conditions and definition of the KPIs to be estimated. Construction of a

simulation matrix according to the prevailing wind directions and wind speeds (SOF’s modeler)

Assignment of CFD runs for execution in the HPC cloud or our private parallel cluster

Summing-up of the results for the estimation of suitable KPIs (2 KPIs per alternative per variant). The

KPIs are delivered via CSV files to KPA tool.

CFD detailed analysis results presentation through videos and 3D representations of suitable flow

quantities (stream lines, pressure isosurfaces etc.) attached to IFC model in MMNav.

7.7.4. 3D Therm Analysis in Detailed Design

CFD analysis for the prediction of microclimate conditions inside buildings provides the capability for

accurate estimation of thermal comfort conditions in buildings and the optimization of HVAC layouts

ensuring that:

interrelated and dynamic aspects of building performance (passive and active elements – energy

demand and supply reduction options) are adequately considered and reflected in the predictions of

energy performance, finally resulting in indoor comfort

the building design performs as close as possible to what is anticipated

not only energy consumption needs are addressed but also the risk of overheating, peak loads and

indoor air quality are assessed

improved decision making is provided so that designers can achieve the optimum and most cost-

effective combination for reduced energy consumption and improved indoor comfort.

3DThermalCFD is a CFD application enhanced with heat transfer equation and buoyancy terms in Navier-

Stokes equations. It is used coupled with Energy Simulations in order to provide detailed data in spaces of

particular interest.

Currently, the coupling of Energy Analysis with the CFD analysis algorithm is a subject of ongoing research

work and a significant amount of manual work is required in order to exchange data/results between the

two types of simulations. This manual work within eeEmbedded is significantly reduced and the coupling

becomes feasible.

The CFD model includes all the HVAC and BACS installation alternatives in order to evaluate the effectiveness

of HVAC systems under both energy performance and thermal comfort criteria. The special characteristics

and the installation of the chosen HVAC systems are taken into consideration and alternative scenarios can

be examined.


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In the Detailed Design phase the thermal comfort KPIs concern the percentage of the occupied space where

thermal comfort requirements are fulfilled.

Examples of suitable KPIs are the Predicted mean vote (PMV), the percentage of people dissatisfied (PPD)

due to discomfort, the percentage dissatisfied (PD) due to draft, and ventilation effectiveness (EN

7730:2005, EN 15251:2007).

CFD detailed results provide answers for:

What parts of the room exposes occupants to the most uncomfortable conditions?

What parts of the room are responsible for wasteful heat gains or losses?

What parts of the room exposes occupants to the highest levels of CO2 and other harmful gases?

Users can easily follow path lines and flow mixing resulting from mechanical or natural ventilation in

order to evaluate the effectiveness of natural or mechanical ventilation systems.

Apart from spatial and temporal 3D representation of KPIs, results interpretation is based on 3D stream lines

“coloured” with temperature (Figure 53 below), spatial distribution of air temperature and air speed and

turbulence intensity distribution in 3D space or at levels of occupants head and legs.

Stream lines coloured with temperature and pressure

Figure 53: CFD results representation for evaluation of KPIs efficiency purposes (Z3 pilot, Stuttgart)


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CFD analysis workflow

Separate IFC model for each alternative which concern the type and the installation of the HVAC systems,

are received for the CFD analysis. The CFD analysis proceeds according to the following steps:

Data import:

i. Space/room geometry (from IFC file)

ii. Operation point of HVAC systems and airflow conditions (flow rate, cross sectional area, point on the

psychrometric diagram) for the scenario of interest (i.t. ｔhe coldest of winter or hottest fay in summer)

iii. Thermal and cooling loads, such as occupant loads, infiltration loads, equipment and lighting loads from

templates

iv. Wall temperatures (from Energy Simulations)

v. Solar gains from glazing surfaces (W/m2 from Energy Simulations)

vi. BACS function

Model simplifications for reasonable CFD analysis (SOF’s modeler)

Imposition of CFD boundary conditions and definition of the KPIs to be estimated. Formulation of

suitable boundary conditions according to HVAC type and operating point (SOF’s modeler)

Assignment of CFD runs for execution in the HPC cloud or our private parallel cluster

Summing-up of the results for the estimation of suitable KPIs. Examples of suitable KPIs are: Predicted

mean vote (PMV), the percentage of people dissatisfied (PPD) due to discomfort, the percentage

dissatisfied (PD) due to draft, and ventilation effectiveness. The KPIs are delivered via CSV files to KPA tool.

CFD detailed analysis results presentation through videos and 3D representations of suitable flow

quantities (stream lines, pressure isosurfaces etc.) attached to IFC model in MMNav.

7.8. LCA and LCC Analysis

The LCC/LCA tool using iTWO® helps in implementations of activity/time-based considerations for a sequence

of alternatives in the project. The LCC/LCA workflow is processed within different modules in iTWO such as

Element Planning; Bill of Quantities (BoQ), Job Estimation, Activity Model, thereby transferring results to each

of these activities to generate the whole life cycle cost and assessment of different alternative within the

project. TWO’s LCC/LCA process is important to identify the key performance indicators (KPI) at each stage over

a period and providing practical guidance on the application of LCC for each use case.

The LCC/LCA model is a link between the CAD model and cost accounting model. The BIM Qualifier provides

users with functions that allow you to check the IFC4 data and correct it, if necessary, or to improve the

quality of the data. Element planning calculates the quantity estimation for different CAD models. The Bill of

Quantities (BoQ) a structured document, using a hierarchy for collecting and summarizing values at each

level in the structure. The Bill of Quantities (BOQ) contains all the works to be cost/ecological assessment in

project alternative. For a project, different BOQs can be defined, structure and referenced with respect to

Model detailing. The job estimation model is used to produce the cost of the BOQ item using the cost code

or commodities. Estimate details are parameterized so that detail resource quantities, productivities and

cost factor are calculated. The activity model links the estimate or BOQ to activity schedule. This allows

estimation using the time dependence variable by linking with the calendar catalogue. This transforms into

activity schedule, which is an important tool for life cycle analysis. The activity model is interlinked with the

resources, which is linked with the cost codes and commodities.


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Figure 54: BIM Qualifier for importing IFC4 files

Figure 55: Element Planning different IFC model

Figure 56: Job estimation for estimation of cost using resources (Cost code/ commodity)


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Figure 57: Activity model inside iTWO for time scheduling

Figure 58: LCC result for KPI Tool for decision making

7.9. Risk Assessment Service

The goal of the risk assessment service is to analyze the effects of uncertainty on a buildings performance.

The focus in eeEmbedded was especially set on energy-related risks. In this context, main contributing

uncertainties have been described in (Gnüchtel et al., 2015) and encompass categories of variables like

thermo-physical properties (e.g. climate and material properties), occupancy and occupant behavior as well

as energy system reliability. The assessment of the resulting energy risk has a large impact for the decision

making process, as the risk has direct influence on the exploitation and maintenance costs. In the pilot

projects and for the proof of concept, the focus has been on climate and occupancy uncertainty, but the

developed approach can also be applied to other types of uncertainty.

In the context of the eeBIM framework, a crucial challenge was the integration of uncertainty information

into the eeBIM Multimodel. The newly developed variation model described in (Pruvost et al., 2017) has

been established with this purpose in mind. In addition to enabling the description of numerous design

alternatives and variants, it also describes stochastic realizations thereof. Stochastic realizations are

generated by the dedicated Sampling Service (SaS) and thus can be applied for each design variant. They

describe several scenarios for the building life cycle that are dependent on aleatory uncertainties, e.g.

climate conditions and occupancy. Unlike design variants, which are configured manually by an end user in

the Multimodel Navigator (MMNav), the stochastic realizations and their respective data variations are

generated automatically by the Sampling Service and are afterwards stored into the Multimodel Container

(MMC). For the sake of the TRNSYS energy simulation, defined within the use case scenarios (Sukumar et al.,

2015) and applied by the pilot projects, each stochastic realization consists of a data sample, which is


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constituted by time series of climate and occupancy variables and thus reflects varying climatic and building

usage conditions over time. After the export of the Multimodel Container from the MMNav, the dedicated

sampling service is functioning as an on-demand service for the preparation of samples for the energy

simulation and can be started directly from the Scenario Manager (ScM). Once started the SaS imports the

MMC and produces data samples. Finally, those are then exported back into the MMC. The samples are

generated on the basis of the background stochastic models described in (Gnüchtel et al., 2015) and

implemented via specific algorithms. In the following we are giving a brief explanation about the modeling

approaches followed for the climate and occupancy variables.

For occupancy sampling, a first-order Markov chain technique has been applied. This technique has been

chosen for its flexibility as well as moderate computational cost. The sampling operates at zone level, where

zone is a flexible term that can reflect a room, a storey or the whole building. As input data functions one

transition matrix per zone type that consists of probabilities, which describe that at a certain point in time,

the number of occupants changes from one amount to another (see Figure 59). The generation of this matrix

is realized from the existing occupancy data of a comparable zone. The sampling itself generates a time

series for each zone, consisting of an occupants number for each time step. Figure 60 shows a graphical

representation of such an output of one day.

Figure 59: Transition matrix for occupancy sampling

Figure 60: Graphical representation of one output from occupancy sampling

For the eeE climate sampling method performed prior to the energy simulation, there were three challenges:


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The first was to generate realistic high-resolution climate samples, consisting of multiple parameters each

(outdoor temperature, wind speed, solar radiation, etc.) and for a time span of up to 30 years. This

disqualifies pure mathematical methods for time series prediction like ARIMA.

Secondly, to use these samples for uncertainty analysis, they need to reflect the natural uncertainty of future

weather events. This disqualifies the artificial averaged data sets typically used in energy simulation (e.g. TRY).

Thirdly, computing time has to be in line with a workflow approach centered on information gain and possible

reevaluation. This disqualifies complicated atmospheric models used by meteorologists for weather prediction.

In view of that, the following approach satisfies all three demands:

For locations of interest a database with the measured weather data of the last years is being created. It is

important that the data sets comply with the requirement for fine resolution of all required parameters,

which means that a time interval of 10 minutes is desired. Any missing data points are filled in with average

values. Additionally, a compromise between high number of data sets and their actuality has to be made.

When performing the targeted uncertainty analysis, each energy simulation run is supplemented with one of

these data sets, chosen randomly from all available sets for the location.

After the simulation of all samples in TRNSYS-TUD (Forns-Samso et al., 2015), the simulation engine provides a set

of KPI values as required by the variation model. In the example of Figure 61 below, the computed KPI was the

energy demand for heating (kWh/m2/year). The figure shows the uncertainty analysis view of the KPA tool in

which the distribution of KPI values for three different design variants are plotted. For each variant a set of around

fifty weather and occupancy samples were used. The figure shows substantial differences between the three

variants, which differ in their energy system technology (district heating, natural gas boiler and biomass boiler

respectively), window-to-wall ratio and other architectural properties. The second example shows less robustness

with regard to uncertainty and has consequently a higher standard deviation. Despite higher mean energy

consumption, the two other alternatives show less volatility in the yearly use of energy.

Figure 61: Visualization of simulation results from performed uncertainty analysis in terms of energy demand for heating by three different design variants (KPA tool)


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On the basis of such a KPI output data set, certain KRIs can be derived in post-processing on the basis of the

value distribution of each KPI, which results from uncertainty for the simulation input. The KRI derived from

the previous data set in the example above is the standard deviation of the heating energy demand.

Following the same approach and using other specific input data and simulation tools, additional KRIs can be

computed. Such KRI can be defined as statistical indicators, deduced from the value distribution of a KPI.

Examples are energy system reliability, energy system maintainability, energy system availability, investment

costs, revenue and so on.

7.10. Multi Key Point Analysis Tool for Decision Making

Decision making, broadly defined, is a combination of interconnected activities that include gathering,

interpreting and exchanging information; creating and identifying alternative courses of action; choosing

among alternatives by integrating the often differing perspectives and opinions of stakeholders; and

implementing a choice and monitoring its consequences. In eeEmbedded we acknowledge the multi-

disciplinary nature of a construction project: it employs several experts performing different types of

analyses, simulations and thus creating a large number of results to be explored and analysed. The most

general case of decision-making within a building design process can be expressed as an end decision that is

the product of a whole series of domain decisions (i.e. financial, environmental, functional and physical) and

the interaction of the interested parties.

In construction projects, group decision making is always preferable compared with individual decision

making for increased effectiveness. However, working in such inhomogeneous group/collaborative

environments is not common and requires the use of methods, methodologies and tools that facilitate a

holistic and collaborative design process. One of the main outcomes of the eeEmbedded project is the

developed Key Point Methodology and the supporting software tools, such as the Scenario Manager,

Multimodel Navigator and the Multi Key Point Analysis (KPA) tool. The combination of the methodologies

and tools will allow project teams to work in a more collaborative and structured way and thus allow them

to making better informed decisions during each the design phase. In the following we describe the

developed Multi Key Point Analysis Tool used to support the decision making process.

The Idea

Due to the complexity of construction projects and the large amount of information created during a design

process, the integration and visualization of data is critical for well-founded decision making. The intention is

to develop a tool that supports multidisciplinary work and enhances collaboration between projects teams.

Additionally, it should help the experts and decision makers in analyzing and evaluating the impacts of Key

Performance Indicators (KPIs) from different design domains. A weighing factor can be added to each KPI or

different KPIs, corresponding to the same evaluation criteria (e.g. environmental or financial), can be

grouped together and receive a shared weighting factor. This process will result in the calculation and

visualization of a Decision Value (DV). The process will give us a better understanding of the analyzed

variants and present a more holistic view of the evaluated criteria as well as the involved variables. The KPA

tool supports teams to work in a more structured way, while also enabling flexibility and interaction within

the analysis of variants and thus facilitating the decision making process. The tool is critical for rapid

comparison of different evaluation criteria.


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The Innovation

The KPA tool is developed as a web-based decision making application that uses a graphical multi-attribute

utility analysis to evaluate and compare alternatives based on Key Performance Indicators. Most of the

calculations and all of the visualizations are computed and rendered in the browser, while only some minor

touches are done on the server side. The tool contains three main modules: The first module is the

simulation synthesis, which performs sensitivity and uncertainty analysis. The sensitivity analysis calculates

and visualizes the standardized coefficients for each design parameter in relation to each KPI and is used to

prioritize decisions for the design parameters that have stronger influence on the KPIs. Uncertainty analysis

is used to visualize the fluctuation of the KPIs in the presence of uncertain parameters, such as weather data

or user profiles. The employed sensitivity analysis was developed in the previously funded EU project ISES. In

eeEmbedded we added the uncertainty analysis visualization as shown in Figure 61 in section 7.9.

The second module is the KPI analysis, which uses advanced plotting graphics for multi-dimensional data

visualization, such as hyper radial visualization, parallel coordinate plot and radar charts. All the plots are

developed and used for analysis purposes, thus allowing the user to investigate the relationship between the

various variables in depth. By using these plots the user has the option to thoroughly investigate a wide

range of alternatives and data used to create those alternatives. Figure 62 shows the KPIs and design

parameters in one plot, visualized as a parallel plot. Each polyline is the representation of one design variant.

The example case shows a large number of variants represented by the polylines. However, the capability of

the parallel coordinate plot in the tool allows for making a criteria selection based on different requirements

for each KPI, reducing the number of alternatives based on these requirements. The bolded polylines on

Figure 63 represent alternatives that could be analyzed in more detail as they fall within the project

requirements and represent the most optimal solutions.

Figure 62: Parallel coordinate plot showing all possible alternatives and relationships between KPIs and design parameters


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Figure 63: Filtered parallel coordinate plot representing a reduced number of alternatives based on criteria selection and highlighting the better one (thick curves)

The third module is the Decision Value (DV) analysis which graphically represents the preferences of the

decision makers related to the project goals. The Decision Value is calculated as a weighted sum of the

results of fitness functions over each KPI. This allows prioritizing KPIs by means of a weighting factor. The

user can set up their requirements and group the KPIs with a weighing factor within the Scenario Manager or

through the requirements setup in the KPA tool. The main purpose of the tool is to reduce the wide range of

variants to the ones that better represent the stakeholder preferences of the initial project setup. However,

this does not mean that the highest DV alternative has to be chosen, stakeholders can choose a reduced

number of alternatives that could be analyzed in further detail using an iterative process.

Figure 64: Decision value representation


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Benefits to end users

Integrating results from analysis and simulations from different software tools and having these results

represented with advanced graphics facilitates the decision making process. Consequently, it creates better

understanding of the influence of different design parameters towards the design variants and offers a

comprehensive way to manage them. Calculating and visualizing the Decision Values benefits end users for

having a holistic view of the project priorities.


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8. Evaluation of the eeEmbedded Platform

The platform is evaluated employing the validation and verification steps as has been detailed in the

Deliverable D9.4 (Sprenger & Poloczek, 2017). The qualitative and quantitative parameters have been

gathered during the assessment of the platform and the evaluation has is based on the developed

prototypes, the availability of data, the working methods, the business and social impacts, the future

perspectives as well as possible standardization steps.

There are two methods used for the platform evaluation. The first method is the Cost Benefit Analysis (CBA)

presented in Table 2 below. Its purpose is to investigate the integrity of the platform and the methodology

with regard to their balance of costs against the provided benefits. The benefits from the development and application of the platform are compared to the potential risks, which may occur during a project using the eeEmbedded platform. The CBA focuses mainly on the efficiency and quality of the process and the quality and costs of the resulting design. To measure these, the targeted and design results, i.e. TO-BE and AS-IS KPIs, KDPs and DV, of the pilot projects are used. The improvements towards the processing time is

measured by comparing the different phases of the design process and comparing them to the pilot projects’ results produced by the eeE platform. The second method is the Strength, Weaknesses, Opportunities, Threats (SWOT) analysis summarized in the Table 3. It investigates each component of the platform and is

thereby helping to define the strengths and weaknesses that it offers to the platforms’ users as well as the opportunities to enhance each product and to give it a competitive value. Finally, an insight regarding possible threats involving the usage and development of the components is provided.

Table 2: Cost Benefit Analysis based on the performed scenario validation

Note: steps marked with an asterisk and shown on green background are valid for all phases but were most thoroughly investigated in the Urban Design phase and are therefore shown within this part of the table

Process steps Difference to current practice Benefits Costs

Basis: Urban Design Validation

Project setup and management*

Process management optimization

Process time: No reduction in time regarding BEP

setup, but time saved for coordination in later

stages by elimination of ‘manual’ coordination.

Achieves increased process efficiency.

Process quality: Clear roles, responsibilities and

information exchanges are defined.

Building quality: Ensures building design quality at each milestone. The building fulfils the client

and regulation requirements more closely.

Building costs: Ensures costs remain within budget.

Development of templates

Training for correct handling

Create Design cubature

One standard IFC model and setup for all BIM users, that is maintained throughout the entire duration of the project

Process time: eeE workflow does not require

complicated model in early phases, since the

templates are assigned to wall elements. If many variants shall be analysed, separate models have

to be created for each variant. Out of one variant,

many alternatives can be generated.

Process quality: One common IFC file per alternative for the entire process and shared by all

tools ensures a smooth design process.

Building quality: IFC model is validated by using the ER checks. This provides excellent quality of an

architectural 3D model at an early design stage

ensuring the quality at the later design stages.

Building costs: The IFC model is used for LCC calculations within the cost estimation tool. Costs

of the building and the materials can be predicted

at an early stage of design.

Depending on the complexity

of variants, not all exchange

requirements can currently be

met. Models created in Revit

and exported to IFC4 tend to

fail some ER checks due to

export quality.


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Create

Variants*

Visual and immediate creation

of building variants per element

type.

Process time: The standard variant creation needs

each element to be designed and therefore expert

knowledge is needed for creating the complete

model in Revit. In eeE, predefined energy

templates are ready for assignment for a simple model and automatic template assignment is

possible.

Process quality: Clear visualization of elements with variant data.

Building quality: Quick evaluation of building

energy behaviours subjected to different energy settings.

Building costs: Best quality vs. cost building options can be evaluated by creating many

variants.

A standard naming convention

is needed

Differences of the variants need to be tracked manually

Libraries need constant refinement to be up-to-date

(for materials, countries, costs, etc.)

Quality check –

Exchange

requirements*

Automated model-based

checking of the information

requirements and visual feedback of deviations.

Process time: Compact verification of the model

and all variants saves time. Communication is

more efficient, due to automatically receiving

made-to-measure information in each task,

instead of defining and requesting the needed information.

Process quality: Each variant is checked against exchange requirements. ER checks make the

process smoother since many errors are detected

at an early stage.

Building quality: Ensures wholeness of the building’s BIM model for the purpose of the

simulations. Increases the quality and reliability of

simulations, which in turn assess building quality.

Building costs: Supports by providing a complete

model for cost analysis.

As long as IFC standards are

developing and the software

tools used are changing their

requirements, ER Checks need

to be updated.

A programmer is needed to formulate the ER checks (e.g.

SPARQL)

Quality check –

KDP check*

Automated model-based

requirements check. Visualises the deviations from KDP.

Process time: Quick setup and management of

KDPs within one table. Variant management is

easier and faster: those that do not fulfil the

requirements are discarded and the time that would be used to simulate them is saved.

Process quality: Structured database and documentation with all KDPs, which can be

checked anytime during the design process.

Visualization of KDPs in the MMNav.

Building quality: Advanced and early management of the requirements towards the building. KDP

check guarantees that requirements are fulfilled or

that deviations are being flagged.

Building costs: Limitations can be introduced for

KDPs that will later influence the LCC costs simulation.

Requires lots of initial input

before the design process

starts. However, tables can be

reused.

Changes in the requirements need changes in the setup.

Extension for additional checks needs expert knowledge.

CFD Wind simulation

Continuous feedback from the CFD analysis to the architect

and therefore easy decision-

making about the cubature,

which are critical for all the consequent design phases.

Process time: The pre-processing time for the

preparation of models for the CFD analysis is

drastically reduced and becomes feasible even for

non-CFD-experts. CFD analysis becomes accessible to architects from a very early design phase on.

Computing time is also reduced by performing

simulation runs in the cloud.

Process quality: The accuracy of the results is improved due to the consideration of construction

details and the building’s surrounding environment.

The capability for automatic formulation of the CFD

model via the incorporation of templates in the

Detailed Design phase and the coupling with Energy Analysis significantly improves the quality and the

efficiency of the simulation.

Extension and automation of

the coupling of CFD analysis

with Energy simulation is

needed for a more accurate analysis and to render the

coupled analysis feasible for the

whole building.


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Building quality: Supports the decision for the best

architectural alternative: CFD analysis in the

Detailed Design phase incorporates the energy analysis detailed thermal comfort criteria. The

selected KPIs will act as metrics to measure a

building’s absolute energy use performance, in

order to benchmark against similar buildings or with best-practices and assess energy efficiency

measures within buildings.

Building costs: Improved decision making is

provided and designers can achieve the optimum

and most cost-effective combination for reduced energy consumption as well as improved indoor

and outdoor comfort.

Stochastic Sampling*

Currently the process is done as a stand-alone. In the eeE

workflow the stochastic

sampling is based on and transferred together with the

simulation results. It enables

the use of stochastic based

variable ranges to enhance design variant options.

Process time: One-click analysis.

Process quality: Stochastic sampling covers (1)

occupancy profiles related to the space types as well as (2) weather data related to the location of

the building. Because of the integration of the

sampling service in the Scenario Manager

accessing the sampling service is easy to handle. The results of the sampling services will be

automatically integrated in the job description

handed over via Multimodel Container. The

preparation of the energy analysis will include the generated samplings.

Building quality: Thanks to the risk analysis, the influence of comfort and quality parameters can

be evaluated.

Building costs: Risk analysis supports more reliable calculation of costs, thus better decision values can

be achieved.

The more samples are analysed,

the longer the simulation takes.

Energy simulation*

Currently the standard approach is to develop a

completely new 3D energy

model. In the eeE workflow, energy simulation is based on

the existing IFC model provided

by an architect.

Process time: Depends on the complexity of the model and time intervals used within the simulation. The time is significantly shortened due to the cloud environment. Simulation results are automatically incorporated. The results are summarized in KPIs making decision making easier and faster. The utilization of an existing IFC model makes the process faster comparing to create an entirely new 3D energy model.

Process quality: No expert knowledge is required to

develop the energy analysis model, since it is based on the IFC model and MMC content. In current practice the exchange between the architectural and simulation software are often not working very well, here the IFC model is transferred almost without a problem.

Building quality: Energy analysis is included at a very early design stage and applied for different variants. Many factors, e.g. wall construction materials, window-wall ratio, CFD simulation, are taken into account to provide the most reliable energy results for the building.

Building costs: Energy analysis enhances LCC cal-culations in predicting the best cost-efficient variant.

The Energy Simulation has

specific demands toward ER and therefore towards

modelling (e.g. 2nd level space

boundaries are required).

Life Cycle Cost

Analysis*

All chosen construction types,

energy system types and

simulation results for an alternative-variant combination

are merged into one IFC, which

allows automation and inte-

gration of LCC in a very early design stage.

Process time: LCC simulation results and domain

decisions are much quicker due to storing the

results in KPIs. The automation saves time in

alternative-variant processing and comparison.

Process quality: Variants can be filtered.

Building quality: Supports identifying the alternative-variant combination meeting the

sustainable criteria and the budget.

Initial iTWO template setup

requires lots of manual work

inside the software.


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Building costs: Supports identifying the alternative-

variant combination with the optimized LCC.

Decision making*

Weighted decisions from all the analyses and simulations based

on compiled Decision Values,

which have been defined by the decision maker at project start

within the KP setup.

Process time: Much faster and compact overview

of all the variants and their quality. Faster cross-domain analysis and cross-domain decisions.

Increased cost-effectiveness of evaluation of the

alternatives and variants.

Process quality: All results are clearly visualized.

Building quality: Provides a clear indication of a building with best quality with regard to the

integrated and prioritized design quality goals

coming from all domains.

Building costs: Supports to identify a building with best quality and cost combination.

Weighting needs to be checked

to ensure the right priorities are correctly represented

throughout the decision making

process.

Basis: Early Design Validation

HVAC Design Feasibility study of more va-riants can be made than in the

current practise possible.

Process time: More variants can be analysed in the

same time.

Process quality: Design issues are easily com-municated with the other design members via BCF.

Building quality: Energy and comfort optimisation by interactive consideration of building and

systems variants.

Building costs: Balanced life cycle costs

Setup of generic HVAC

templates

BACS Design BACS engineer is involved

earlier in the design process. Process time: Knowledge-based design support

fastens the early concepts.

Process quality: Knowledge-based support for the

BACS designer to comply with EN15232.

Building quality: Optimised energy efficiency and comfort based on early analysis of control

strategies.

Building costs: Balanced LCC for the clients’ needs

based on integrated costs.

Setup a database with control

strategies, components and

costs or invest in the eeBACS

Wizard once the product is ready for the market.

FM Strategy FM is involved earlier in the design process.

Process time: Knowledge-based support allows for

earlier analysis of maintenance strategies.

Process quality: Integrated analysis of

maintenance strategy and LCC.

Building quality: Optimised quality over the whole

life cycle.

Building costs: Optimised costs based on

clients/users’ needs over the whole life cycle

Setup a generic database of

maintenance cycles, service lives and maintenance

strategies.

Basis: Detailed Design Validation

Comfort simulation

In Detailed Design, currently the coupling of Energy Analysis with

the CFD analysis algorithm is

subject of ongoing research and a

significant amount of manual work is required to exchange

results between the two types of

simulations. This manual work is

significantly reduced within eeEmbedded and the coupling

becomes feasible.

Process time: The pre-processing time for the

preparation of models for the CFD analysis is

drastically reduced and becomes feasible even for non-CFD experts. CFD analysis becomes accessible

to architects in a very early design phase.

Computing time is reduced by assigning simulation

runs in the cloud. The capability for automatic formulation of the CFD model and the coupling

with Energy Analysis significantly improves the

quality and the efficiency of the simulation

Building quality: CFD analysis in the Detailed

Design phase incorporates the energy analysis detailed thermal comfort criteria. Not only energy

consumption needs are addressed but also the risk

of overheating, peak loads and indoor air quality is

assessed (Detailed Design).

Building costs: Improved decision making is

provided: designers can achieve the optimum and most cost-effective combination for reduced

energy consumption and improved indoor and

outdoor comfort.

Extension and automation of

the coupling of CFD analysis

with Energy simulation needs a more accurate analysis to

render the coupled analysis

feasible for the whole building

(Detailed Design).


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Table 3: SWOT Analysis

SWOT / VDL components

Strengths Weaknesses Opportunities Threats

Basis: Urban and Early Design Validation

Scenario

Manager

Well-structured set up of a BIM

Execution Plan. BIM process

coordination and information

management. Accessible for

users at any time. The updating

of the information occurs

automatically since all the tools

are connected with ScM. The

workflow is tracked across the

design steps & analyses. Access

to all project/task messages.

Lack of data exchange flexibi-

lity with the tools as it de-

pends on the Project Setup.

Once the process workflow is

setup and the process is

started, there is no possibility

to amend the process.

Design a process with tasks that

can be done in parallel can be

developed. Process templating

can be developed for the ability

to reuse processes or sub-

processes.

Security issues of the

software. Maintenance,

administration and secu-

rity care is required.

MMNav Graphical user interface for

clear analysis of KDP. Quick

generation of variants based

on a single 3D model through

easy access to predefined

templates. It is possible to

include and link external data

to the model. Offers Web-

based presentation of a 3D

model and automatic assign-

ment of element types based

on Omni Classes defined

within the Project Setup.

It is not possible for end users

to integrate new templates

within MMNav. Not possible to

instantly change properties

within a template in MMNav.

Reviewing the model in MMNav

is flexible and clients have the

possibility to access the model

at any time. It is possible to

extend the template access to a

template management system

for end users, which is

integrated in the Project Setup.

This should allow for the

creation and updating of

templates.

Large size projects require

a reliable and fast internet

connection as well as fast

server.

KDP and

ER Checking

Automatic checks for KDPs and

exchange requirements, saves

time and manual work. This

automatic check detects more

errors than a manual analysis of

the model.

Without detailed IFC know-

ledge, it is not possible to

specify the checks from scratch.

User-friendliness needs to be

improved

Setup can be improved to be

more user-friendly. The checks

for the geometry could be

expanded and further deve-

loped. An appropriate graphical

user interface could be a

solution to avoid using expert

knowledge and setup KDPs and

ERs can be reused.

Defining the wrong rules

could lead to

misinterpretation of the

results.

TRNSYS Automated pre-processing

covers the transfer of the IFC

model to the simulation model,

by enhancing the model with

additional information, inclu-

ding assigned templates.

IFC model does currently not

include construction templates

chosen. They are loaded sepa-

rately as a .csv file as an input

of the analysis.

Enhancing the capabilities of

the IFC model interpretation

and thus, for example, omit the

additional .csv file.

IFC standards are influ-

encing the interpretation

of the IFC model need to

be adjusted to updates.

3DWind Completely integrated into

the workflow and uses the

shared BIM model. Produces

simulation data as KPIs and

returns those back to the

process workflow. Usage of

the KPI allows for easier

interpretation of the analysis

results and the classification

of the building.

Automatic import of IFC

model saves manual work for

the preparation of the CFD

model.

Simulation takes a significant

amount of time, which slows

the entire design process.

Tool can only be used by a CFD

expert. There is a lack of

visualisation of the results

within MMNav as results can

be only attached in it.

Enhanced automation of the

CFD model generation from the

IFC model in the CFD geome-

trical modelling environment.

This includes geometry

modifications in order to fulfil

the requirements for a feasible

CFD analysis.

Improving the speed and

robustness of CFD simulations

is an ongoing research field.

Not suitable for cases of

rapid urban development,

where the topography

changes drastically around

the building of interest.


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SWOT / VDL components

Strengths Weaknesses Opportunities Threats

iTWO -

LCC/LCA

Automated life cycle cost and

life cycle analysis for each

variant.

With minor exceptions, the

data within iTWO cannot be

reused with other BIM

software programs. The initial setup of iTWO

templates is a complicated

manual process.

Until now, iTWO does not provide any user interface for

LCC and LCA calculation.

The generated data could be

provided using an open schema

and be treated independently of

the program’s functionalities, to enable back-end data com-

munication. Providing a user-

interface for the LCC and LCA

calculation would highly simplify the procedure for the end users.

The MMC approach should be

integrated into the iTWO process

to increase the automation of recognition of templates, vari-

ants, contents, materials etc.

Shared data management

is considered to be the

ongoing trend in develop-

ments, which the tool is lacking.

No implementation of

LCC/LCA user interface is

foreseen in the immediate future.

Multi-KP

tool

Easy and transparent decision

making and visual analysis of

alternatives and variants. User-friendly: supports cross-

domain analysis, decision

making and multi-criteria

analysis.

Since it is a web-based tool,

there is no direct connection

to the data or the results: It needs a connection to the

internet to function.

The visualisation depends on

the accuracy of the data provided and the format

specified.

The tool is not BIM com-

patible: Data is agnostic, which could also be a benefit.

For someone who is not aware

of KDPs, KPIs are a better guide

for result interpretation and could be added. Plotting and

analysis types can be expanded.

Algorithms to automatically

optimize the selection of alternatives and narrow the

number of alternatives can be

implemented.

The software has security

issues.

Basis: Early Design Validation

eeBACS

Wizard

Control strategies based on an

Early Design model.

Incorporates the IFC/HVAC

IfcDistributionSystem, specific

enumeration and relation of

HVAC components to spaces.

Proposes BACS equipment

(sensors, actors) and their costs.

There is no 3D representation

within the tool and it is limited

to EN15252 energy efficiency

classes in the current proto-

type.

Enhancing and expanding the

database and involve the BACS

designer early in the process

Depends on a correct

HVAC model.

Basis: Detailed Design Validation

3DTherm Completely integrated into the

workflow and uses the shared

BIM model. Produces simu-lation data as KPIs and returns

these back to the design work-

flow. Automatic import of the

enhanced IFC model with leads to the saving of manual work.

The most important strength of

the CFD analysis lies in the

automatic coupling with energy

analysis application, which provides a great technological

and scientific advantage for the

detailed energy analysis of

buildings.

Simulation takes a significant

amount of time that slows the

entire design process down.

In order to formulate a fea-

sible geometrical model and

boundary conditions, manual

work is needed for the proper simplification of HVAC, BACS,

furniture and other devices

that are considered for energy

balance of each space.

Increase automations for both

the preparation of suitable

models for CFD analysis and the knowledge-based interpretation

of analysis results to make it more

accessible for non-expert end

users (architects, HVAC designers)

The AEC market has continuously

increasing needs for a combined

CFD and Energy Analysis.

New energy saving

technologies and zero

energy buildings require continuous adaptation of

the techniques for the

generation of suitable CFD

models.

The results show that the workflow and most tools are very useful and beneficial for the process quality and

help to shorten the time needed to perform such a process. Furthermore, most of them have an

advantageous influence on the building costs and quality. The prototypical state of the tools means that

many tools have the need to improve their user interface for easier access and that some may be only be

operated by experts or after extensive training.


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9. Exploitation of the eeEmbedded Platform

The main goal of the eeEmbedded project was the development of a stochastic and model-based Virtual

Energy Lab Platform and virtual design office for integrated engineering design and the development of new

products and components facilitating energy-efficient planning. The developed ICT system is a modular

platform, from which each eeEmbedded partner did develop and configured specific modules or

combinations of modules, as required for their individual exploitation needs. The platform will therefore be

the main exploitable prototype and each eeEmbedded partner and any other interested party can exploit the

platform in their own configuration as well as with third-party software exploiting the eeEmbedded data

models and interfaces, as long as the third-party software complies to the established BIM-related

information management standards (IFC, BCF) and some specific energy-related modelling requirements.

The integrated eeEmbedded platform will be exploited:

as a technical Virtual Energy Lab to study new products (building components, technical components)

and services before their realization and to perform an in-depth analysis of poor performance under

lifecycle conditions in order to undertake the right tuning decisions,

as a holistic virtual engineering design office for the design and redesign of facilities and buildings to

study alternatives in depth and under various life-cycle conditions, in order to come up with the best

balanced design decision for the facility owner,

as a system analysis tool to analyze the energy and emission behavior of existing facilities and buildings,

in order to find out their weak components or instances of degraded operation and to make suggestions

to the owner for the redesign, retrofitting or reengineering of the operational processes.

The individual components will be used as standalone tools or in a bundle with other tools providing BIM-

based interoperability infrastructure. They will be exploited and marketed separately by each eeEmbedded

partner as they deem suitable for their individual exploitation needs.

Achieved Commercially Exploitable Results

Open BIM Platform Services

Scenario Manager and Multimodel Navigator

User Interface and Workflow Management of the eeEmbedded Virtual Lab Platform enabling to assign tasks, actions and data, deadlines and dependencies; Navigation and control of all project variants and displaying the results from all analysis tools, thereby supporting an informed decision making; Free of charge basic functionalities of MMNav including a SDK for 3rd parties and an open API.

Ontology (OVS), ER Check, KDP Check

Fast validation of Key Point values and rules of standard design codes of an eeBIM Model according to pre-defined rule sets.

Templates

Construction types, Materials with physical properties, future integration of Libraries, definition of alternative sub-processes to the pre-defined scenarios via Business Process Modelling Notations (BPMN) that may contain a lot of conflicts


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Commercial End User Applications or Plug-In Modules

3D Wind CFD Analysis Tool

Aerodynamic analysis of buildings to optimize shape and position of a building according to criteria such as energy consumption, wind comfort, wind potential, natural ventilation design

3DThermal CFD Analysis Tool

Detailed energy analysis in buildings for the prediction of indoor climate conditions toward the fulfilment of user-defined thermal comfort require-ments

iTWO LCC/LCA Life cycle cost and analysis estimation for all investment and running costs regarding even environmental impacts, like energy and CO2 consumption

Multi KPA Tool Comparing different design alternatives to find the best solution; Display of user-definable performance values (Key Points); Various visualizations within the tool or via the Multimodel Navigator

TRNSYS Energy simulation for calculation of the thermal behavior of buildings taking into account energy systems and HVAC components

eeBACS Wizard Determining correct BACS variant(s) according to already calculated Key Design Parameters

EDM Model Server BIM server which can merge IFC Files from different disciplines with application in energy analyses, HVAC design and lifecycle cost calculations

Extensions to existing Commercial Applications

Allplan (NEM) General Architectural Design CAD Software, capable to manage multiple configurations using parametric modelling

DDS-CAD MEP (DDS) HVAC Design capable to generate multiple configurations using parametric modelling and to produce domain BIM models according to various LOD/LoD agreements

Case Builder BACS (SAR)

Software for handling building automation projects; contains energy-efficient strategies and methods

Granlund Designer (GRA)

Designing the MEP equipment in a construction project taking into consideration FM requirements


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10. Conclusions

This report described the essential results of the eeEmbedded project which have been thoroughly validated

and evaluated by end users from the design (architecture, HVAC, energy), construction and FM domains. The

results showed that the goals of the project set up at the outset have been successfully achieved. A new

design methodology building upon the use of a comprehensive hierarchy of Key Points (DVs, KDPs, KPIs,

KRIs), advanced ICT technologies and standardised data models has been developed and a consistent

adaptable and extensible Virtual Design Office platform has been implemented. Much of the realized

software tools and services are thereby well prepared for short-term practical exploitation in AEC industry

context. Through the use of both storage and compute cloud facilities scalability problems have been

successfully solved. By applying templates, filtering and advanced multimodel techniques a possibility of

investigating a large number of design variants has been found, which can be used not only by large

companies but also by SMEs which are typical for the design landscape in the AEC domain.

However, while the results are overall positive, there is still research and development work needed to

achieve even better design solutions with regard to optimal energy use (especially with regard to renewable

energy), sustainability and cost effectiveness. Various modelling issues need yet to be solved as the related

data standards and the respective market and academic tools have not yet reached full maturity. For

example, the transformation of BIM data to the computational input models needed by analysis and

simulation tools is still a challenging problem for many complex practical cases. Interoperability of tools and

services also remains an issue, especially when detailed data is addressed, as for example by sophisticated

simulations or in detailed design. Last but not least, issues related to the BIMification of the existing building

stock need to be addressed to be able to apply the eeEmbedded methodology efficiently in retrofitting and

refurbishment projects.

Concluding, it has to be noted that this report only provides an overview of the major project findings.

Detailed results are documented in a rich set of public deliverable reports which will be available via the

project’s web site (http://www.eeembedded.eu). More importantly, commercial exploitable results are

expected to emerge soon as outlined shortly in the preceding chapter.

http://www.eeembedded.eu/


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http://www.mefisto-bau.de/


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Grille T, Pruvost H. & Scherer R.J. (2016): Stochastic Analysis for Design Space Exploration and Building

Performance Optimisation, Proc. CESBP Central European Symposium on Building Physics and BauSIM

2016, pp. 369-375, http://www.cesbp2016.de/cesbp

Gudnasson G., Balaras C., Dascalaki E., Katranuschkov P., Laine T., Grunewald J., Protopsaltis B., Mansperger

T., (2012): ISES Deliverable D4.1: Technical specification of the overall framework and the principal

energy profile and consumption patterns, © ISES Consortium, Brussels.

Guruz R. & Scherer R.J. (2014): Sustainable energy entrepreneurship through architectural design: a key

point controlled method, Entrepreneurship and sustainability issues, Vol. 2, pp. 60-73,

Entrepreneurship and sustainability center, Lithuania, http://jssidoi.org/jesi/aims-and-scope-of-

research/

Häfele, K.-H., Brenner, J., FZKViewer V 4.7 (Build 905, 2017-02-10), Karlsruhe, Germany.

Hollingsworth D. (1994): The Workflow Reference Model, © Workflow Management Coalition.

ifcDoc Tool [Online], http://www.buildingsmart-tech.org/specifications/specification-tools/ifcdoc-tool

ISO 16739 (2013): Industry Foundation Classes (IFC) for data sharing in the construction and facility

management industries, International Organization for Standardization, Geneva.

ISO 29481-1 (2016): Building information models -- Information delivery manual -- Part 1: Methodology and

format, International Organization for Standardization, Geneva.

ISO 29481-2 (2012): Building information models -- Information delivery manual -- Part 2: Interaction

framework, International Organization for Standardization, Geneva.

ISO/IEC 19510 (2013): Information technology -- Object Management Group Business Process Model and

Notation, International Organization for Standardization, Geneva.

Jaunzens D., Warriner D., Garner U. & Waterman A. (2001): Applying facilities expertise in building design,

Construction Research Communications Ltd. by permission of Building Research Establishment Ltd.,

London, UK

Jensen, P. A. (2009) Design Integration of Facilities Management: A Challenge of Knowledge Transfer.

Architectural, Engineering and Design Management, 5, pp. 124-135.

Laine T., Forns-Samso F. & Kukkonen V. (2016): Visual support for multi-criteria decision making, Proc.

ECPPM 2016, pp. 371-376, CRC Press, UK

McArthur J., Herrera N. & Mantha P. (2014): International Sustainability Systems Comparison - Key Inter-

national Sustainability Systems: Energy and Water Conservation Requirements, CoreNet Global by Ove

Arup & Partners Ltd., March 2014.

OmniClass (2017): OmniClass, a strategy for classifying the built environment, http://www.omniclass.org/

Pruvost H., Grille T. & Scherer R.J. (2016): An IT-based holistic methodology for analysing and managing

building lifecycle risk, Proc. ECPPM 2016, pp. 377-386, CRC Press, UK

Pruvost H., Katranuschkov P. & Scherer R.J. (2017): Multimodel-based exploration of the building design

space and its uncertainty, Proc. Sustainable Places 2017, Middlesborough, UK

Scherer R.J., Katranuschkov P. & Baumgärtel K. (2016): Open eeBIM Platform for Energy-Efficient Building

Design, Proc. ECPPM 2016, pp. 387-395, CRC Press, UK

Scherer R.J., Katranuschkov P. & Guruz (2015): Energy-Efficient BIM Lab, Proc. Lake Constance 5D

Conference, pp. 391-405, Hochschule Konstanz, Germany

http://www.cesbp2016.de/cesbp

http://www.buildingsmart-tech.org/specifications/specification-tools/ifcdoc-tool

http://www.omniclass.org/


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Scherer, R. J. & Schapke, S.-E. A distributed multi-model-based management information system for

simulation and decision making on construction projects. In: Advanced Engineering Informatics, 25

(2011), pp. 582-599. Elsevier, The Netherlands, 2011.

Shao Y., Geyer P., Lang W. (2014): Integrating requirement analysis and multi-objective optimization for

office building energy retrofit strategies, http://dx.doi.org/10.1016/j.enbuild.2014.07.030, Elsevier.

Solibri Model Viewer version 9.6.21 [Online], https://www.solibri.com/products/solibri-model-viewer/

Todinov M. T. (2006): Risk-based reliability analysis and generic principles for risk reduction, Elsevier Science.

Related eeEmbedded Deliverables

Calleja-Rodríguez G. (2017): eeEmbedded Deliverable D2.5: New ways of holistic working for energy

optimized and embedded building, 100 p., © eeEmbedded Consortium, Brussels.

Dangl G., Linhard K. & Katranuschkov, P. (2016): eeEmbedded D8.2: Collaborative Methods, 62 p., ©

eeEmbedded Consortium, Brussels.

Forns-Samso, F., Sukumar, A., Kroner, M., Geissler, M.-C., Calleja-Rodríguez, G., Kaiser, J., Pruvost, H., Grille,

T. & Schär, R. (2015): eeEmbedded D3.2: Sustainability prognosis, quality and ROI methods, 100 p.,


Geißler M.-C., Guruz R. & van Woudenberg W. (2014): eeEmbedded D1.2: Use case scenarios and

requirements specification, 78 p., © eeEmbedded Consortium, Brussels.

Gnüchtel S., Kaiser J., Pruvost H., Stenzel P. Grille, T. & Schär R. (2015): eeEmbedded D3.1: Stochastic, risk

and vulnerability models and control strategies, 127 p., © eeEmbedded Consortium, Brussels.

Guruz R., Calleja-Rodriguez G. & Geißler M.-C. (2015a): eeEmbedded D2.1 Holistic multi-disciplinary Key

Point-based design framework, 85 p., © eeEmbedded Consortium, Brussels.

Guruz R., Calleja-Rodríguez G. & Geißler M.-C. (2015b): eeEmbedded D2.1 Holistic KPI-based design metho-

dology, 87 p., © eeEmbedded Consortium, Brussels.

Kaiser J. & Stenzel P. (2015): eeEmbedded D4.2: Energy System Information Model - ESIM, © eeEmbedded

Consortium, Brussels.

Pruvost, H., Grille, T., Baumgärtel, K., Schülbe, R. & Kadolsky, M. (2017): eeEmbedded D6.5: Optimization of

multi-model criteria, © eeEmbedded Consortium, Brussels.

Sprenger W. & Poloczek S. (2017a): eeEmbedded D9.2: Validation Test Sites Model, 34 p., © eeEmbedded

Consortium, Brussels.

Sprenger W. & Poloczek S. (2017b): eeEmbedded D9.4: Validation and verification of the KPI design

methodology, 60 p., © eeEmbedded Consortium, Brussels.

Sukumar A., Tøn A., Linhard K., Mrazek E., Mosch M. & Katranuschkov P. (2015): eeEmbedded D8.1: SOA

Platform Architecture, © eeEmbedded Consortium, Brussels.

Zellner R. & Kaiser J. (2015): eeEmbedded D2.2: Templates for fast semi-automatic detailing, 48 p.,

© eeEmbedded Consortium, Brussels.

Zellner R. & Mrazek E. (2017): eeEmbedded D7.4: Multi‐model navigation and visualization service, 54 p., ©


https://www.solibri.com/products/solibri-model-viewer/


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Acronyms

AEC Architecture, Engineering and Construction

API Application Programming Interface – a set of clearly defined methods of communication

between various software components

ARCH Architectural – Most often used to shorten descriptions, i.e. ARCH model

ARIMA Autoregressive integrated moving average – a statistic model to better understand the data or

to predict future points in the series

BACS Building Automation and Control System

BCF BIM Collaboration Format, a data standard to exchange communications about building models

BIM Building Information Modelling/Model – describes either the method of BIM, i.e. the all-

encompassing workflows and processes for a digital design process, or the BIM model, i.e. all

the files that make up the complete building model

BoQ Bill of Quantities

BPMN Business Process Model and Notation – a graphical representation for specifying business

processes in a business process model

BREEAM Building Research Establishment Environmental Assessment Methodology – a method of

assessing, rating, and certifying the sustainability of buildings

CAD Computer-Aided Design

CBA Cost Benefit Analysis

CFD Computational Fluid Dynamics – a branch of fluid mechanics that uses numerical analysis and

data structures to solve and analyse problems that involve fluid flows

CSV Comma-Separated Values

DGNB Deutsche Gesellschaft für Nachhaltiges Bauen e.V. – The German Sustainable Building Council

was founded to promote sustainable and economically efficient building

DV Decision Value – subcategory of the eeEmbedded Key Points, relates to aggregated values,

which support and facilitate a decision

eeE eeEmbedded – official shortcut acronym for the project

eeBIM energy enhanced BIM – either describes BIM data that has been enriched with energy data or

the general modelling method, which includes energy considerations

eeeBIM energy efficient enhanced BIM – either describes BIM data that has been enriched with energy

data or the general modelling method, which includes energy considerations

ER Exchange Requirement – defines data, values and attributes that are required to be available or

to fulfil certain criteria, so that exchange between two software tools or actors can happen

without errors

ESIM Energy System Information Model – the newly developed system model in eeE for the energy

systems

FM Facility Management

HPC High Performance Computing – describes computer or in general computing above the average

computing power of personal computers


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HVAC Heating, Ventilation and Air Conditioning

ICT Information and Communications Technology

ID Identifier – mostly refers to identifiers within software

IDM Information Delivery Manual – refers to ISO 29481-1:2010, specifying a methodology and

format for the development of an information delivery manual (IDM)

IFC Industry Foundation Classes

JSON JavaScript Object Notation – an open-standard file format

KDP Key Design Parameter – one subcategory of Key Point in the eeEmbedded framework, generally

describes mandatory design features for a building

KP Key Point – main aspect of the eeEmbedded design methodology, comprehensible and

aggregated indicators for a buildings performance

KPA Key Point Analysis tool – a software tool developed by the eeEmbedded partner GRA, provides

features to visualize the performance (KP) of a building

KPI Key Performance Indicator – one subcategory of Key Point in the eeEmbedded framework,

generally depicts performance values of a building that are not explicitly visible, i.e. simulation

results

KRI Key Risk Indicator – a subcategory of Key Point in the eeEmbedded framework, generally depicts

uncertainties and risks and is used for the stochastically expression of building performance

LCA Life Cycle Assessment – the domain and process to assess the life cycle of a building

LCC Life Cycle Costing – closely tied to LCA above; calculates the costs corresponding to the life

cycle(s) of a building

LEED Leadership in Energy and Environmental Design – a standard for green building design

LoD Level of Detail

LOD Level of Development

MEP Mechanical, Electrical and Plumbing – a domain of a building and the building design process

MMC Multimodel Container

MMNav Multimodel Navigator – a software tool developed by the partner NEM, main purpose is the

visualisation and navigation of the eeEmbedded Multimodel

MSM EDMmodelServerManager – the management component of the EDM server

MTTF Mean Time To Failure

MTTR Mean Time To Repair

MVD Model View Definition – MVD are subsets (subformats) of a BIM model format and do allow for

certain “views” upon a BIM model

OVS Ontology Verification Service – a software component and service of the eeEmbedded platform

developed by TUD-CIB, the OVS validates the BIM model with regard to data integrity as well as

calculates and checks the Key Points for a building design

OWL Web Ontology Language – a family of knowledge representation languages

PD Percentage Dissatisfied – a term used for comfort analysis within a building

PM Property Management


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PMV Predicted Mean Vote

PPD Percentage of People Dissatisfied

RDF Resource Description Framework – a semantic web data model

ScM Scenario Manager – a software tool of the eeEmbedded platform, which is orchestrating and

managing the overall design process and the data transfer

SDK Software Development Kit

SOA Service Oriented Architecture – a set of principles and methodologies for designing and

developing software in the form of interoperable services

SOAP Simple Object Access Protocol – a protocol specification for exchanging structured information

in the implementation of web services in computer networks

SQL Structured Query Language – a domain-specific language designed for managing data held in a

relational database management system or for stream processing in a relational data stream

management system

SPARQL SPARQL Protocol and RDF Query Language – a RDF query language

SPARUL declarative data manipulation language that is an extension to SPARQL; it provides the ability to

insert, delete and update RDF data held within a triple store or quad store.

SPFF STEP Physical File Format – see STEP

SSP Server Side Plug-in

STEP Standard for the Exchange of Product model data – The ISO 10303 family of standards for the

computer-interpretable representation and exchange of product manufacturing information

SWOT Strengths, Weaknesses, Opportunities and Threats analysis – a structured planning method that

evaluates those four elements of an organization, project or business venture

SysML Systems Modelling Language – a general-purpose modelling language for systems engineering

applications

TRNSYS Transient System Simulation – a simulation program primarily used in the fields of renewable

energy engineering and building simulation, here it refers to the development of the Technische

Universität Dresden

TRY Test Reference Year

UML Unified Modelling Language – a widely spread software modelling language

WWR Window(-to-)Wall Ratio – describes the ration between walls and the windows in it, most often

relates to the (outer) surface area of the wall and window respectivly

XML Extensible Markup Language

XPX EXPRESS-X – query and mapping language for EXPRESS models (ISO 10303-11)

https://en.wikipedia.org/wiki/Declarative_programming

https://en.wikipedia.org/wiki/Data_manipulation_language

https://en.wikipedia.org/wiki/SPARQL

https://en.wikipedia.org/wiki/Resource_Description_Framework

https://en.wikipedia.org/wiki/Triplestore

https://en.wikipedia.org/wiki/Quad_store


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Partner Abbreviations

TUD-CIB Technische Universität Dresden – Institute of Construction Informatics, Germany (coordinator)

TUD-IEC Technische Universität Dresden – Institute of Power Engineering, Germany

BAM Koninklijke BAM Groep NV, The Netherlands

BDE BAM Deutschland AG, BAM subsidiary in Germany

BNL BAM Utiliteitsbouw b.v., BAM subsidiary in the Netherlands

CEM Centro de Estudios Materiales y Control de Obras S.A. (CEMOSA), Spain

DDS Data Design System ASA, Norway / Germany

EAS Fraunhofer Gesellschaft zur Förderung der Angewandten Forschung e.V. – Institute for

Integrated Circuits, Germany

EPM Jotne EPM Technology AS, Norway

GRA Granlund Oy, Finland

IAB iabi – Institute for Applied Building Informatics, Germany

NEM Nemetschek Allplan Slovensko S.R.O., Slovakia

OPB OBERMEYER Planen + Beraten GmbH, Germany

RIB RIB Information Technologies AG, Germany

SAR Fr. SAUTER AG, Switzerland

SOF SOFiSTiK Hellas AE, Greece

STA STRABAG AG, Austria

ZUE Ed. Züblin AG, STA subsidiary in Germany


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PART 2 – subject to confidentiality –


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Potential impact

The requirements of the EC regarding the reporting of the project’s potential impact are defined

as follows:

A plan for use and dissemination of foreground (including socio-economic impact and target groups for the

results of the research) shall be established at the end of the project. It should, where appropriate, be an

update of the initial plan in Annex I for use and dissemination of foreground and be consistent with the report

on societal implications on the use and dissemination of foreground.

The plan should consist of:

Section A

This section should describe the dissemination measures, including any scientific publications relating to

foreground. Its content will be made available in the public domain thus demonstrating the added-value

and positive impact of the project on the European Union.

Section B

This section should specify the exploitable foreground and provide the plans for exploitation. All these data

can be public or confidential; the report must clearly mark non-publishable (confidential) parts that will be

treated as such by the Commission. Information under Section B that is not marked as confidential will be

made available in the public domain thus demonstrating the added-value and positive impact of the project

on the European Union.

In addition a report section regarding the societal implications of the project has to be included accorind to a

predefined template.

These sections are presented on the following pages completing the Final Project Report.

D2.2 Templates for fast semi-automatic detailing

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Section A (public)

This section includes two tables:

Table A1: List of all scientific (peer reviewed) publications relating to the foreground of the project sorted by year (from oldest to newest).

Table A2: List of all dissemination activities (publications, conferences, workshops, web sites/applications, press releases, flyers, articles published in the

popular press, videos, media briefings, presentations, exhibitions, thesis, interviews, films, TV clips, posters).

TABLE A1: LIST OF SCIENTIFIC (PEER REVIEWED) PUBLICATIONS

No. Title Main author Title of the periodical

or the series

Number, date or

frequency Publisher

Place of publication

Year of publication

Relevant pages

Permanent identifiers1 (if available)

Is/Will open access2 provided to this publication?

1 Sustainable energy entrepreneurship through architectural design: a key point controlled method

Guruz R., Scherer R. J.

Entrepreneurship and sustainability issues

Vol. 2 Entrepreneurship and sustainability center

Lithuania 2014 pp. 60-73 http://jssidoi.org/jesi/aims-and-scope-of-research/

yes

2 Building Requirements as Basis for a Key Point controlled Design Method

Guruz R., Scherer R.J.

2nd Int. Conference on ICT for Sustainable Places (ICT4SP2014) - 2nd EeB KPIs Workshop

Nice, France 2014 pp. 58 http://sustainable-places.eu/wp-content/uploads/2014/10/SP14_Proceedings_281014.pdf

no

3 Processes and Requirements for an eeEmbedded Virtual

Geißler M. C., van Woudenberg W., Guruz R.

eWork and eBusiness in Architecture, Engineering and Construction

CRC Press Vienna, Austria

2014 pp. 887-892 http://www.crcnetbase.com/doi/book/10.1201/b17396

no

1 A permanent identifier should be a persistent link to the published version full text if open access or abstract if article is pay per view) or to the final manuscript accepted for publication

(link to article in repository). 2

Open Access is defined as free of charge access for anyone via Internet. Please answer "yes" if the open access to the publication is already established and also if the embargo period for open access is not yet over but you intend to establish open access afterwards.

http://sustainable-places.eu/wp-content/uploads/2014/10/SP14_Proceedings_281014.pdf





http://www.crcnetbase.com/doi/book/10.1201/b17396




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or the series

Number, date or

frequency Publisher


Year of publication

Relevant pages



Design Laboratory

4 Towards a KPI-controlled holistic design method for eeBuildings

R. J. Scherer, R. Guruz, G. Calleja-Rodriguez, M.-C. Geißler



2014 pp. 879-885 http://www.crcnetbase.com/doi/book/10.1201/b17396 no

5 An ontology framework for improving building energy performance by utilizing energy saving regulations

K. Baumgärtel , M. Kadolsky , R. Scherer



2014 pp. 519-528 http://www.crcnetbase.com/doi/book/10.1201/b17396

no

6 BIM-based Virtual Design Laboratory for energy-efficient embedded buildings

Geißler M.C., van Woudenberg W.

Lake Constance 5D-Conference 2015 Proceedings

Hochschule Konstanz. University of applied sciences

Konstanz, Germany

2015 pp. 380-390

no

7 Energy-Efficient BIM Lab

Scherer R.J., Katranuschkov P., Guruz R.

Lake Constance 5D-Conference 2015 Proceedings

Hochschule Konstanz. University of applied sciences

Konstanz, Germany

2015 pp. 391-405

no

8 Ontology-controlled Energy Simulation Workflow

K. Baumgärtel, M. Kadolsky, R.J. Scherer

Sustainable Places 2015 Proceedings

Savona, Italy 2015 http://sustainable-places.eu/wp-content/uploads/2015/09/FP2_Ken_B.pdf

no

9 Knowledge management framework for monitoring systems improving building energy efficiency

Mathias Kadolsky, Ronny Windisch, Raimar J. Scherer

2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) Proceedings

IEEE 2015 pp. 33-38 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7175848

no

10 Model based processes at large

RIB BBB Booklet University of Aachen

Aachen, Germany

2015 no







http://sustainable-places.eu/wp-content/uploads/2015/09/FP2_Ken_B.pdf




http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7175848




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or the series

Number, date or

frequency Publisher


Year of publication

Relevant pages



infrastructure projects

11 Towards a Workflow-Driven Multi-model BIM Collaboration Platform

Mario Gürtler, Ken Baumgärtel, Raimar J. Scherer

Risks and Resilience of Collaborative Networks

463 Springer International Publishing

Cham, Germany

2015 pp. 235-242 http://link.springer.com/chapter/10.1007/978-3-319-24141-8_21 no

12 Visualization of Facilities Management KPIs during Early Design Using BIM

Forns-Samso F., Laine T., Geissler M.-C.

Proceedings of the CIB World Building Congress 2016

TUT - Tampere University of Technology

Tampere, Finland

2016 pp. 1001-1011

https://tutcris.tut.fi/portal/files/6187048/WBC16_Vol_5.pdf yes

13 Optimizing Energy-Efficient Building Design Using BIM

Katranuschkov P., Scherer R.J., Hoch R.

Proceedings of the 16th International Conference on Computing in Civil and Building Engineering ICCCBE 2016

Osaka University Osaka, Japan 2016 pp. 1765-1772

http://www.see.eng.osaka-u.ac.jp/seeit/icccbe2016/Proceedings yes

14 Stochastic Analysis for Design Space Exploration and Building Performance Optimisation

Grille T, Pruvost H., Scherer R.J.

Proceedings of the CESBP Central European Symposium on Building Physics and BauSIM 2016

Fraunhofer IRB Dresden, Germany

2016 pp. 369-375 http://www.cesbp2016.de/cesbp

no

15 eeBIM LAB – Towards a coherent green building design process

Guruz R., Katranuschkov P., Scherer R.J.

Proceedings of the CESBP Central European Symposium on Building Physics and BauSIM 2016

Fraunhofer IRB Dresden, Germany

2016 pp. 391-395 http://www.cesbp2016.de/cesbp

no

16 KPI visualisation supporting the involvement of facility managers in early

Forns-Samso F., Laine T

Proceedings of CFM’S Second Nordic Conference

Polyteknisk Forlag

Copenhagen Denmark

2016 pp. 68-77 http://orbit.dtu.dk/files/125899567/CFM_Nordic_Conference_2016_proceedings_1.pdf

yes

http://link.springer.com/chapter/10.1007/978-3-319-24141-8_21



https://tutcris.tut.fi/portal/files/6187048/WBC16_Vol_5.pdf








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or the series

Number, date or

frequency Publisher


Year of publication

Relevant pages



design

17 Collaboration requirements and interoperability fundamentals in BIM based multi-disciplinary building design processes

Calleja-Rodriguez G., Guruz R., Geißler M.C., Steinmann R., Linhard K. Dangl G.


CRC Press London, UK 2016 pp. 349-354 https://www.crcpress.com/eWork-and-eBusiness-in-Architecture-Engineering-and-Construction-ECPPM/Christodoulo18u-herer/p/book/9781138032804

no

18 Task-Specific Linking for Generating an eeBIM Model based on an Ontology Framework

Kadolsky M., Scherer R.J.


CRC Press London, UK 2016 pp. 363-370 https://www.crcpress.com/eWork-and-eBusiness-in-Architecture-Engineering-and-Construction-ECPPM/Christodoulou-Scherer/p/book/9781138032804

no

19 Visual support for multi-criteria decision making

Laine T., Forns-Samso F., Kukkonen V.


CRC Press London, UK 2016 pp. 371-376 https://www.crcpress.com/eWork-and-eBusiness-in-Architecture-Engineering-and-Construction-ECPPM/Christodoulou-Scherer/p/book/9781138032804

no

20 An IT-based holistic methodology for analysing and managing building lifecycle risk

Pruvost H., Grille T., Scherer R.J


CRC Press London, UK 2016 pp. 377-386 https://www.crcpress.com/eWork-and-eBusiness-in-Architecture-Engineering-and-Construction-

no

https://www.crcpress.com/eWork-and-eBusiness-in-Architecture-Engineering-and-Construction-ECPPM/Christodoulo18u-herer/p/book/9781138032804









https://www.crcpress.com/eWork-and-eBusiness-in-Architecture-Engineering-and-Construction-ECPPM/Christodoulou-Scherer/p/book/9781138032804






















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or the series

Number, date or

frequency Publisher


Year of publication

Relevant pages



ECPPM/Christodoulou-Scherer/p/book/9781138032804

21 Open eeBIM Platform for Energy-Efficient Building Design

Scherer R.J., Katranuschkov P., Baumgärtel K.


CRC Press London, UK 2016 pp. 387-395 https://www.crcpress.com/eWork-and-eBusiness-in-Architecture-Engineering-and-Construction-ECPPM /Christodoulou-Scherer /p/book/9781138032804

no

22 Automatic ontology-based green building design parameter vari-ation and evaluation in thermal energy buil-ding performance analyses

Baumgärtel K., Scherer R.J.


CRC Press London, UK 2016 pp. 667-672 see 21 above

no

23 Automated alternatives analysis procedure using key points me-thod for multidiscipli-nary energy efficient building design projects

G. Calleja-Rodriguez, R. Guruz, M.-C. Loeffler

Automation in Construction

Elsevier Germany 2017

yes

24 An intelligent platform for thermal energy analyses

K. Baumgärtel, P. Katranuschkov, R.J. Scherer

Automation in Construction

Elsevier Germany 2017 yes

25 Scenario Manager: In-tegrated and innova-tive concept for pro-cess and information management based on BIM execution plan

M.-C. Loeffler, G. Calleja-Rodriguez, R. Guruz

Proceedings of the International Research Conference

Salford, UK 2017 http://conference.org.uk/international-research-week/




https://www.crcpress.com/eWork-and-eBusiness-in-Architecture-Engineering-and-Construction-ECPPM%20/Christodoulou-Scherer%20/p/book/9781138032804








http://conference.org.uk/international-research-week/




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or the series

Number, date or

frequency Publisher


Year of publication

Relevant pages



digitalization and key points

26 Analysis of risk in building life cycle coupling BIM-based energy simulation and semantic modelling

Pruvost, H., Scherer, R.J.

Proc. Creative Construction Conference 2017

Procedia Engineering, Elsevier

Primošten, Croatia

2017

no

27 Multimodel-based exploration of the building design space and its uncertainty

Pruvost, H., Katranuschkov, P., Scherer, R.J.

Sustainable Places 2017 Proceedings

Teesside University

Middles-brough, UK

2017

yes

28 Entwicklung eines Informationsmanagementsystems und einer Entscheidungsplattform für die Zusammen-arbeit aller Projekt-beteiligten

Löffler, M.C. BBB Booklet Stuttgart, Germany

2017

no


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TABLE A2: LIST OF DISSEMINATION ACTIVITIES IN CHRONOLOGICAL ORDER

No. Type of activities Main leader Title Date/Period Place Type of audience Size of

audience Countries addressed

1 Web sites/ Applications

TECHNISCHE UNIVERSITAET DRESDEN

Project Web Site 01/12/2013 Internet - www.eeembedded.eu

Scientific community (higher education, Research) - Industry - Civil society - Policy makers - Medias

n/a World



SharePoint 31/10/2013 Internet - https://eembd.cib.bau. tu-dresden.de

Scientific community (higher education, Research) - Industry

n/a Consortium countries

3 Flyers CENTRO DE ESTUDIOS DE MATERIALES Y CONTROL DE OBRA SA

Project flyer 15/05/2014 Europe Scientific community (higher education, Research) - Industry - Civil society - Policy makers - Medias

>200 Europe

4 Oral presentation to a wider public

KONINKLIJKE BAM GROEP NV

BAM Internal Event 27/03/2014 Stuttgart, Germany Industry 30 Germany

5 Press releases CENTRO DE ESTUDIOS DE MATERIALES Y CONTROL DE OBRA SA

eeEmbedded Newsletter Nr.1

31/03/2014 Europe Scientific community (higher education, Research) - Industry - Civil society - Policy makers - Medias

n/a Europe



Presentations from prac-tice for BIM project study

10/04/2014 Stuttgart University, Germany

Scientific community (higher education, Research)

20 Germany

7 Oral presentation to a scientific event


An ontology framework for improving building energy performance by utilizing energy saving regulations

18/09/2014 10th European Conference on Product & Process Modelling (ECPPM2014)


130 Europe



Processes and Requirements for an eeEmbedded Virtual Design Laboratory

19/09/2014 10th European Conference on Product & Process Modelling (ECPPM2014)


153 Europe

9 Oral presentation to TECHNISCHE Towards a KPI-controlled 19/09/2014 10th European Scientific community (higher 130 Europe


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a scientific event UNIVERSITAET DRESDEN

holistic design method for eeBuildings

Conference on Product & Process Modelling (ECPPM2014)

education, Research) - Industry


NEMETSCHEK ALLPLAN SLOVENSKO SRO

Nemetschek BIM Meeting

19/09/2014 Munich Industry 7 Germany



Nemetschek Development Department Meeting in Bratislava

22/09/2014 Bratislava, Slovakia Industry 73 Europe



Building Requirements as Basis for a Key Point controlled Design Method

01/10/2014 2nd International Conference on ICT for Sustainable Places (ICT4SP2014)


200 World




n/a Europe



Virtuelles Planungslabor für nachhaltige Stadtquartiere

22/10/2014 Internet - https://www.bam-deutschland.de/news/883?page=1

Industry n/a Germany




n/a Europe


GRANLUND OY Design methodology, optimization of energy usage

14/04/2015 Innovation Platform, Brussels, Belgium

Industry 15 Europe


GRANLUND OY Design methodology, BIM systems

17/04/2015 Switzerland Industry 10 Switzerland

18 Press releases NEMETSCHEK ALLPLAN SLOVENSKO SRO

eeEmbedded Methodology/mmNavigator

22/04/2015 ZUG Schweiz Industry 11 Suiss Germany


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GRANLUND OY Granlund Designer, overview

24/04/2015 Energy Seminar organized by Granlund

Industry 200 Finland

20 Organisation of Workshops

CENTRO DE ESTUDIOS DE MATERIALES Y CONTROL DE OBRA SA

1st eeEmbedded Expert Seminar

05/05/2015 Lake Constance 5D-Conference 2015. Constance, Germany


n/a Europe

21 Posters CENTRO DE ESTUDIOS DE MATERIALES Y CONTROL DE OBRA SA

Project Overview 05/05/2015 5D Lake Constance Conference. Constance, Germany


300 Europe

22 Posters TECHNISCHE UNIVERSITAET DRESDEN

Virtual Energy Lab Concept

05/05/2015 5D Lake Constance Conference. Constance, Germany


300 Europe

23 Posters KONINKLIJKE BAM GROEP NV

Generic Use Cases to Facilitate Collaboration



300 Europe

24 Posters CENTRO DE ESTUDIOS DE MATERIALES Y CONTROL DE OBRA SA

Key Points to Support Design Optimization



300 Europe

25 Posters TECHNISCHE UNIVERSITAET DRESDEN

Software Platform Realization



300 Europe

26 Videos SOFISTIK HELLAS AE Impact & Business Cases



300 Europe

27 Videos TECHNISCHE UNIVERSITAET DRESDEN

nD Navigator - CIB 05/05/2015 5D Lake Constance Conference. Constance, Germany


300 Europe

28 Videos GRANLUND OY Multi-KP Decision Support Tool



300 Europe

29 Videos RIB INFORMATION TECHNOLOGIES AG

Life Cycle Simulation 05/05/2015 5D Lake Constance Conference. Constance, Germany


300 Europe

30 Videos INSTITUTE FOR BCF Collaboration 05/05/2015 5D Lake Constance Scientific community (higher 300 Europe


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APPLIED BUILDING INFORMATICS IABI

Conference. Constance, Germany


31 Videos SOFISTIK HELLAS AE CFD Simulation 05/05/2015 5D Lake Constance Conference. Constance, Germany


20 Europe



Overview of the eeEmbedded project



20 Europe



Design Methodology. Requirements and Use Cases



20 Europe



Design Methodology. Key Point Methodology



20 Europe



Software Architecture and Components



20 Europe


FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV

Energy System Information Model



20 Europe


RIB INFORMATION TECHNOLOGIES AG

Main Sponsor Presentation



300 Europe



BIM-based Virtual Design Laboratory for energy-efficient embedded buildings



Europe



Energy-Efficient BIM Lab 05/05/2015 5D Lake Constance Conference. Constance, Germany


300 Europe


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BAM Crossing Boundaries

20/05/2015 BAM SharePoint Industry n/a Europe


GRANLUND OY Working at Bigroom, usecase of Energy Optimization

28/05/2015 Metropolia University of Applied Sciences, Helsinki


30 Finland



Knowledge Management Framework for Monitoring Systems improving Building Energy Efficiency

09/07/2015 EESMS 2015-Environmental, Energy and Structural Monitoring Systems


n/a World

43 Thesis Fr. Sauter AG Lösungs-Bibliothek Assistent

14/08/2015 FHNW Brugg-Windisch Scientific community (higher education, Research)

n/a Switzerland



Ontology-controlled Energy Simulation Workflow

16/09/2015 Sustainable Places 2015


>200 World



Model based processes at large infrastructure projects

17/09/2015 BBB Construction Congress, Aachen


100 Europe


GRANLUND OY Project overview 21/09/2015 Innovation Platform, San Francisco, USA

Industry 15 World



Sustainability of construction projects

29/09/2015 RIB Americas Conference at Georgia Tech, Atlanta

Industry 120 World


Green Building 30/09/2015 Mail/Letter Scientific community (higher education, Research) - Industry

> 5000 Europe


eeEmbedded 30/09/2015 Munich Industry 5 Germany

50 Press releases CENTRO DE ESTUDIOS DE MATERIALES Y


30/09/2015 Europe Scientific community (higher education, Research) - Industry -

n/a Europe


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CONTROL DE OBRA SA Civil society - Policy makers - Medias



Ein Jahr BIM-Forschung mit eeEmbedded und Selfie

01/10/2015 Internet - http://www.bam.eu/

Industry n/a Germany



eeEmbedded LinkedIn Page

05/10/2015 Internet - https://www.linkedin.com/company/eeembedded

Scientific community (higher education, Research) - Industry - Civil society - Medias

n/a World



Towards a workflow-driven multi-model BIM collaboration platform

06/10/2015 PRO-VE'15 (16th IFIP WG 5.5 Working Conference on Virtual Enterprises)


50 World

54 Posters NEMETSCHEK ALLPLAN SLOVENSKO SRO

eeEmbedded: Software Platform Realization

08/10/2015 Eventreihe ALLPLAN 2016. The Way BIM Works. Leipzig, Germany


150 Germany


eeEmbedded: Impact and Business Cases

08/10/2015 Eventreihe ALLPLAN 2016. The Way BIM Works. Leipzig, Germany


150 Germany



28/10/2015 Eventreihe ALLPLAN 2016. The Way BIM Works. Stuttgart, Germany


150 Germany


eeEmbedded: Impact and Business Cases

28/10/2015 Eventreihe ALLPLAN 2016. The Way BIM Works. Stuttgart, Germany


150 Germany



29/10/2015 Eventreihe ALLPLAN 2016. The Way BIM Works, Munich , DE


180 Germany

59 Posters NEMETSCHEK ALLPLAN SLOVENSKO

eeEmbedded: Impact 29/10/2015 Eventreihe ALLPLAN 2016. The Way BIM

Scientific community (higher 180 Germany


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SRO and Business Cases Works. Munich, DE education, Research) - Industry



eeEmbedded - 1st Conference and Road show

01/10/2015-01/11/2015

Series of webinars, webex


> 60 Europe



eeEmbedded: Link Model

23/03/2016 Linked Data Workshop, Dubln


40 World




n/a Europe



eeEmbedded: multi model

11/04/2016 Building Smart International Summit, Rotterdam


200 World



eeEmbedded Methodology an mmNavigator

28/04/2016 Technische Universität München

Industry 19 Germany



eeEmbedded Methodology an mmNavigator

29/04/2016 Technische Universität München


15 Germany



2nd eeEmbedded Expert Seminar

01/06/2016 CIB World Building Congress 2016. Tampere, Finland


20 World

67 Exhibitions CENTRO DE ESTUDIOS DE MATERIALES Y CONTROL DE OBRA SA

Booth in the WBC16 01/06/2016 CIB World Building Congress 2016. Tampere, Finland


n/a World


GRANLUND OY Visualization of Facilities Management KPIs during Early Design Using BIM

02/06/2016 CIB World Building Congress 2016. Tampere, Finland


> 60 World

69 Oral presentation to TECHNISCHE eeEmbedded: 29/06/2016 SUSTAINABLE Scientific community (higher 200 World


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a scientific event UNIVERSITAET DRESDEN

presentation of project results

PLACES 2016 – UNIVERSITY OF PAU AND BASQUE COUNTRY, Anglet




Building Requirements as Basis for a Key Point controlled Design Method

30/06/2016 ID@50 Integrated Design Conference. Bath, England


>100 Europe



eeEmbedded - Fasttrack 19/07/2016 The Edge, Deloitte, Amsterdam

Industry > 30 Europe


FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV

Potentiale des BIM-basierten Entwurfs und Betrieb in der Gebäudeautomation

09/08/2016 Automation 2016 / VDI-Konferenz Gebäudeautomation 2016


100 Germany


GRANLUND OY KPI visualisation suporting the involvement of facility managers in early design

29-30/08/2016

CFM’S 2nd Nordic Conference


> 60 Europe



eeBIM LAB – Towards a coherent green building design process

16/09/2016 CESBP/ BauSIM 2016. Dresden, Germany


Europe



Modeling , Information Managament and Simu-lation for Energy Efficient Design Embedded in the Neighbourhood, 6 papers

08/09/2016 ECPPM 2016 Limassol, Cyprus


40 Europe


INSTITUTE FOR APPLIED BUILDING INFORMATICS - IABI

Collaboration require-ments and interopera-bility fundamentals in BIM based multi-disciplinary building design processes



30 Europe

77 Oral presentation to GRANLUND OY Visual support for multi- 09/09/2016 ECPPM 2016 Scientific community (higher 40 Europe


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a scientific event criteria decision making Limassol, Cyprus education, Research) - Industry



Automatic ontology-based Green Building design Parameter Variation and Evaluation in Thermal Energy Building Perfor-mance Analyses



150 Europe

79 Exhibitions KONINKLIJKE BAM GROEP NV

Booth at the Royal BAM Autumn Meeting

13/09/2016 BAM Autumn Meeting Industry 200 Europe and International



Automatische Prüfung und Filterung in BIM mit Model View Definitions

19/09/2016 Forum Bauinformatik 2016, Hannover, Deutschland

Scientific community (higher education, Research)

80 Germany




n/a Europe



Workshop on Construction - New Digital Sector

13/10/2016 University of Malaga, Spain


30 Europe

83 Exhibitions FR. SAUTER AG BIM World Munich 29-30/11/2016

Munich, Germany Scientific community, Industry, Policy makers, Media

>2000 Germany



Meeting of the group "City of the Future"

01/03/2017 online Scientific community (higher education, Research) - Industry

23 Spain


STRABAG AG 3rd Conf. and roadshow: buildingSMART International Summit

04/04/2017 buildingSMART International Summit, Barcelona, Spain


30 Europe



Overview of the whole eeE plattform from a constructions company view

04/04/2017 buildingSMART Workshop in Barcelona

Scientific community - industry 200 Europe

87 Press releases CENTRO DE ESTUDIOS eeEmbedded Newsletter 21/04/2017 Europe Scientific community (higher Europe


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DE MATERIALES Y CONTROL DE OBRA SA

Nr.7 education, Research) - Industry - Civil society - Policy makers - Medias



Process and information management for sustainable project development

08/05/2017 buildingSMART Workshop in Mainz

Scientific community - industry 15 Europe



4th Conference and roadshow: Workshop in Mainz

08/05/2017 Mainz, Germany Scientific community (higher education, Research) - Industry

50 Germany


ALLPLAN Supports Three EU Projects for the Future of Building

13/06/2017 online Industry > 100.000

Germany, Swiss, Austria,

Worldwide



Four steps closer to a full set of interoperable tools for designing energy effi-cient buildings workshop"

29/06/2017 Sustainable Places 2017, Middlesbrough, United Kingdom

Scientific community (higher education, Research) - Industry - Policy makers

> 60 Europe



Scenario Manager: In-tegrated and innovative concept for process and information management based on BIM execution plan digitalization and key points

11-12/09/2017

International Research Conference 2017, Salford, UK

Scientific community - industry tbc World


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Section B (Confidential3 or public: confidential information to be marked clearly) Part B1 The applications for patents, trademarks, registered designs, etc. shall be listed according to the template B1 provided hereafter. The list should, specify at least one unique identifier e.g. European Patent application reference. For patent applications, only if applicable, contributions to standards should be specified. This table is cumulative, which means that it should always show all applications from the beginning until after the end of the project.

TEMPLATE B1: LIST OF APPLICATIONS FOR PATENTS, TRADEMARKS, REGISTERED DESIGNS, ETC.

Type of IP Rights: Confidential Foreseen embargo date

dd/mm/yyyy Application reference(s)

(e.g. EP123456) Subject or title of application

Applicant(s) (as on the application)

N/A

Note: The products of eeEmbedded are the novel design methodology and software services and tools which cannot be patented. Therefore, this table is

left empty.

3 Not to be confused with the "EU CONFIDENTIAL" classification for some security research projects.


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Part B2

Type of Exploitable Foreground

Description of exploitable foreground

Confidential YES/NO

Foreseen embargo

date dd/mm/yyyy

Exploitable product(s) or measure(s)

Sector(s) of application

Timetable, commercial

or any other use

Patents or other IPR

exploitation (licences)

Owner & Other Beneficiary(s)

involved

Commercial exploitation of R&D results

eeE Virtual Lab Platform - BIM-based collaborative design and simulation platform. The Platform supports the novel eeEmbedded key point based design methodology by providing an integrated information management framework including BIM and energy system information models (ESIM) for designing energy-efficient buildings and their optimal energetic embedding in the neighborhood of surrounding buildings and energy systems.

NO NO Software and Consulting

J62.0 - Computer programming, consultancy and related activities

2018 eeE Partners (OWNER)


Multimodel Framework - Conceptual Framework, Schemas and Reference Implementation for the integration of heterogeneous design data from distributed information sources for the needs of energy-efficient design, LCC/LCA and facility management



2018 ISO/NP21597 (collaboration with COINS and Mefisto projects)

TU Dresden, eeE Partners (OWNER)


Risk Assessment Service - A service related to the eeEmbedded platform and integrated within the eeE Scenario Manager to enable consideration of design and life cycle risks as measurable key indicators for comparing variant designs



2018 R project (free software)

TU Dresden, eeE Partners (OWNER)


Scenario Manager - Main user interface to the virtual lab platform for the energy-optimised design and other processes.

NO NO Software J62.0 - Computer programming, consultancy and related activities

2018 TU Dresden (OWNER)


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Confidential YES/NO

Foreseen embargo

date dd/mm/yyyy




or any other use




involved


Multimodel Navigator - Navigation to control all Building Variations and support decisions by proper displaying of results.


2018/2019 Nemetschek (OWNER)


Ontology (OVS), ER Check, KDP Check - Fast validation of key point values and rules of standard design codes according to pre-defined rule sets


2018/2019 TU Dresden (OWNER) + eeE Consortium


Templates - Construction types, Materials with physical properties, future integration of Libraries, definition of alternative sub-processes to the pre-defined scenarios via Business Process Modelling Notations (BPMN) that may contain a lot of conflicts


2018/2019 TU Dresden (OWNER) + eeE Consortium


3DWind - Aerodynamic analysis of buildings to optimise shape and position of a building according to criteria such as energy consumption, wind comfort, wind potential, natural ventilation design


2020 SOFiSTiK (OWNER)


3DThermalCFD - CFD End user application /Analysis Tool for the detailed energy analysis in buildings for the prediction of indoor climate conditions toward the fulfilment of user-defined thermal comfort requirements


2020 SOFiSTiK (OWNER)


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Confidential YES/NO

Foreseen embargo

date dd/mm/yyyy




or any other use




involved


iTWO – LCC/LCA - Life cycle cost and analysis estimation for all running costs regarding even environmental impacts, like energy and CO2 consumption


after 2018 RIB (OWNER)


Multi-KP Tool - Compare different alternatives to find the best solution. Visualisation can be done in the Multimodel Navigator


after 2018 Granlund (OWNER)


TRNSYS - Energy simulation for calculation of the thermal behaviour of buildings taking into account energy systems and HVAC components. Extension of Commercial Applic.



after 2018 TU Dresden (OWNER)


EPM BIM server - BIM server which can merge IFC Files from different disciplines, energy analysis, cost calculations



after 2018 Jotne (OWNER)


Allplan – Extensions to General Architectural Design CAD Software, able to design multiple configurations using parametric modelling


after 2018 Nemetschek (OWNER)


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Confidential YES/NO

Foreseen embargo

date dd/mm/yyyy




or any other use




involved


eeE Software Services – Software Services offered by NEM enhanced by eeEmbedded functionality


after 2018 Developments of NEM Soft-ware & Partners within the eeE Consortium

Nemetschek Software (OWNER)


DDS-CAD MEP – Extensions to HVAC Design able to generate multiple configurations – using parametric modelling and produce models according to LOD/LoD agreements


after 2018 DDS (OWNER)


Case Builder BACS – Extensions to Software for handling building automation projects, contains energy-efficient strategies and methods



after 2018 Sauter (OWNER)


Granlund Designer – Extensions / Design the MEP equipment in a construction project



after 2018 Granlund (OWNER)


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Confidential YES/NO

Foreseen embargo

date dd/mm/yyyy




or any other use




involved


eeE Design Methodology –Will be integrated to CEMOSA services related to design and refurbishment of singular buildings

Medium Term: eeE Design Methodology will be adopted to integrate it in services related to design and refurbishment of infrastructures

NO NO Consulting F41.1.0 - Development of building projects

after 2018 eeE Consortium CEMOSA (OWNER)


Scenario Manager –Will be integrated to CEMOSA design processes




Key Point Analysis Tool – Will be integrated to CEMOSA design processes for evaluation of buil-dings and infrastructures designs



optimised design methodologies for...

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