intelligent services for energy-efficient design …ises.eu-project.info/documents/ises-d9.2.pdf ·...

INTELLIGENT SERVICES FOR ENERGY-EFFICIENT DESIGN AND LIFE CYCLE SIMULATION

D9.2: End user report on the Virtual Energy Lab pilot

Responsible Authors:

Costas Balaras, Elena Dascalaki

Co-Authors:

Ken Baumgärtel, Peter Katranuschkov, René Hoch, Gudni Gudnason, Byron Protopsaltis, Theodora Pappou, Tuomas Laine, Tobias Mansperger,

Robert Klinc, Symeon Christodoulou, Uroš Leskovšek

SEVENTH FRAMEWORK PROGRAMME OF THE EUROPEAN COMMUNITY (EC GRANT AGREEMENT N° 288819)

Due date: 01.12.2014 Issue date: 03.12.2014

Nature: Other

D9.2 End user report on the Virtual Energy Lab pilot

Version 1.0 ISES – Intelligent Services for Energy-Efficient Life Cycle Simulation

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© ISES Consortium http://ises.eu-project.info/

Start date of project: 01.12.2011 Duration: 36 months

Organisation name of lead contractor for this deliverable:

NATIONAL OBSERVATORY OF ATHENS (NOA)

History

Version Description Lead Author Date

0.1 Initial draft C. Balaras, E. Dascalaki (NOA) 16.09.2014

0.1.1 First Version C. Balaras (NOA) 26.09.2014

0.2 Second Version All partners 27.10.2014

0.3 Third Version All partners 7.11.2014

0.3c Third Version - revised All partners 11.11.2014

0.4 Pre-Final Version All partners 25.11.2014

1.0 Checked and approved Final version TUD-CIB/NOA 03.12.2014

Copyright

This report is © ISES Consortium 2014. Its duplication is restricted to the 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

Balaras C., Dascalaki E., Baumgärtel K., Gudnason G., Katranuschkov P., Hoch R, Protopsaltis B., Pappou T., Laine T., Mansperger T., Klinc R., Christodoulou S., Leskovšek U. (2014): ISES D9.2: End user report on the Virtual Energy Lab pilot © ISES Consortium, Brussels.

Acknowledgements

The work presented in this report has been conducted in the context of the seventh framework programme of the European community project ISES (n° 288819). ISES is a 36-month project that started in December 2011 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, Coordinator), OLOF GRANLUND OY (Finland), UNIVERZA V LJUBLJANI (Slovenia), SOFISTIK HELLAS A.E. (Greece), NYSKOPUNARMIDSTOD ISLANDS (Iceland), NATIONAL OBSERVATORY OF ATHENS (Greece), LEONHARDT ANDRÄ UND PARTNER BERATENDE INGENIEURE VBI GMBH (Germany) and TRIMO INZENIRING IN PROIZVODNJA MONTAZNIH OBJEKTOV, D.D. (Slovenia). This report was created through the joint effort of the above organisations.

Project of the 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)

http://ises.eu-project.info/



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TABLE OF CONTENTS

EXECUTIVE SUMMARY .................................................................................................................. 4

1 INTRODUCTION ...................................................................................................................... 5

2 ENERGY EFFICIENT BUILDING DESIGN WITH ISES ..................................................................... 8

3 USING THE ISES VIRTUAL LAB PLATFORM .............................................................................. 10

3.1 Overall Approach ..................................................................................................................... 10

3.2 The Pilot Buildings ................................................................................................................... 12

3.3 IT Environment ........................................................................................................................ 16

3.4 Preparing Energy Simulations with the ISES nD Navigator ..................................................... 19

3.5 Delivering Results with ISES .................................................................................................... 25

3.6 Platform Performance Benchmarking ..................................................................................... 40

4 SWOT ANALYSIS ................................................................................................................... 46

4.1 Strengths ................................................................................................................................. 46

4.2 Weaknesses ............................................................................................................................. 49

4.3 Opportunities .......................................................................................................................... 52

4.4 Threats..................................................................................................................................... 53

5 CONCLUSIONS ...................................................................................................................... 55

6 REFERENCES ......................................................................................................................... 57




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Executive Summary

The objective of WP9 of ISES is to test and validate the developed Integrated Virtual Energy Lab (IVEL) in practical environments. Accordingly, the ISES prototype has been deployed and demonstrated in real-practice scenarios that target three feedback cycles: design of new products and components, design or re-design of existing facilities for the use of more energy efficient building components, and obtain more accurate results and/or refine use cases for future application. The objective is to illustrate the practical achievements of ISES and to demonstrate the benefits of the developed overall approach and to provide a grounded comparison of state-of-the-art solutions and the results of ISES, comparing the energy demand of one and the same facility designed by current state-of-the-art and new ISES methods, thereby enabling quantification of the exploitation potential and benefits.

This report elaborates Task 9.4: Comparison of state-of-the-art and ISES-based design and further needs, which is the final task of WP9 on the Pilot Virtual Lab and Public Demonstrators. The main goal is to summarize the main ISES findings from the viewpoint of end users and to compare the ISES method against the state-of-the-art practice and the findings of the European FP7 project HESMOS (www.hesmos.eu). The report summarizes the ISES process and tools, demonstrates the process and reviews the results from a demonstration building and finally outlines the main benefits, reveals possible shortcomings and proposes future development needs.

The report is structured into three parts:

Part 1 provides an overview of the ISES process and the IVEL main structure, focusing on the new features and characteristics against the state-of-the-art practice and the findings of the EU project HESMOS.

Part 2 presents the case studies using real buildings to demonstrate the main steps for setting up a project on the IVEL, placing an emphasis on practical issues encountered in the analysis and captures the main results.

Part 3 evaluates the Strengths, Weaknesses, Opportunities and Threats using a SWOT analysis.

Lead partner of the deliverable has been NOA. Structuring and final editing is done by NOA and TUD-CIB. All other partners have contributed their knowledge and results to different sections of the report.


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1 Introduction

Building Information Modelling (BIM) for optimizing the total lifecycle cost of buildings is a major challenge even today.

Inadequate software interoperability and lack of standardization with subsequent loss of information and knowledge in information exchanges between disciplines and lifecycle phases, inconsistent technology adoption among stakeholders and high costs as a result of the fragmented nature of the building industry are just some of the obstacles that architects and engineers face today. However, building design optimization requires a structured approach that enables continuous changes in design variables and assessment on energy consumption. A holistic building design and construction is already introduced in Europe through the energy performance of buildings directive (EPBD). The requirements have been strengthened by the EPBD recast for achieving cost optimal building designs for the life cycle of the building, moving towards nearly zero energy buildings by the end of the decade. BIM and intelligent software systems will play a crucial role in these efforts with improved visualization, increased process coordination and exchange of information in all lifecycle stages, all leading to higher productivity and reduced cost for the design, operation and maintenance of energy efficient buildings.

Computer aided design (CAD) software systems commonly assign three main features to BIMs: ability to store, share and exchange data (files or database); object oriented building model controlled by parametric rules (change to any part of the design automatically reflects to all other parts); ability to link the data model to various types of analysis tools throughout the building lifecycle.

As building design moves past the concept stage, systems require detailed specification. For example, mechanical systems need sizing depending on building envelope material and component selection, since they directly influence the loads. These tasks are usually undertaken through collaboration of various engineering disciplines. Buildings must comply with several codes, such as structural, heating, cooling, ventilation (HVAC), electrical, etc. While each of these capabilities and the systems required to support them may have been identified during the building’s conceptual design, their specification for conformance to codes or certifications require more detailed definition.

Since the 80’s, a large number of energy analysis tools based on building physics were developed, long before the introduction of BIM. For the majority of these tools, a significant effort and time is required in order to prepare the necessary input data to assemble the analytical model required to run the analyses tool.

With common data modelling and exchange standards along with unified Application Programming Interfaces (APIs), a more efficient workflow and automation is possible, allowing multiple experts from different disciplines to intuitively collaborate on the final design. A proper interface between a BIM tool and a specific application assigns the necessary attributes and relations in the BIM tool, compiles an analytical model of the building geometry that contains the necessary data abstracted from the physical BIM model, and supports the data transfer by using a proper format for identifying information to ensure incremental updating on both sides of the exchange.

Almost all existing building analysis software tools require extensive pre-processing of the model geometry, defining building material properties and load conditions (internal heat gains, occupancy schedules, specific indoor conditions for heating or cooling). When BIM tools incorporate the above capabilities, the building geometry can be derived directly from the common model, material properties can be assigned automatically for each analysis and load conditions for an analysis can be stored, edited and applied. Nevertheless, building energy performance simulations (BEPS) usually have specific requirements. For example, one dataset for representing the external building envelope associated with incident solar radiation; a second set for representing the internal thermal zones and




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internal heat gains; and a third set for representing the HVAC installations. Additional data preparation by the user, usually an energy expert, is also commonly required. By default, only the first of these data sets are represented in a typical BIM based design tool.

The Integrated Virtual Energy Lab (IVEL) concept was conceptualized within the EU project HESMOS (http://www.hesmos.eu) for supporting and facilitating the key decision making processes using BEPS for the initial design or the renovation of existing buildings. The main processes and data exchanges were developed on the basis of the Information Delivery Manual (IDM) methodology (ISO 29481-1:2010), which promotes BIM-driven communication with the end users (architect, engi-neer, facilities manager, building operator, owner and public). The HESMOS project integrated in an open platform CAD, simulation, facility management tools and data from building automation systems.

The ISES process moves the IVEL concept one step beyond, by including new features for generating, collecting, managing and efficiently exchanging large amount of data from different sources and tools that are being used by architects and engineers. Various tools for thermal simulation and CFD analysis are readily available, including input data for material properties, climate-weather and occupancies, which also account for their stochastic nature.

More specifically, ISES delivers process and technological innovations that drastically reduce the time and cost to performed whole building energy performance analyses that include high performance cloud based environment for integration of various building energy performance analyses and computational tools, allowing execution of a large number of parallel tasks and thus carry out complex calculations using varied simulation tools in parallel. Currently, integrated computational services on the ISES IVEL include: four energy simulation engines (EnergyPlus, Nandrad, Therakles and Riuska), Computational Fluid Dynamics (CFD) software (SARA), two data analysis and visualization applications, as well as other scientific and engineering software. The ISES cloud Application Programming Interface (API) enables client software to programmatically interact with the computational services in the cloud environment to upload simulation files, initiate and monitor simulation progress and download the simulation results for analyses eliminating the need for end users to interact with the cloud services directly.

In current practise, each simulation tool provides a specialized user interface to create the necessary configuration files to run simulations. In ISES, a unified user interface was developed that enables configuring the different energy simulation engines, each using its proprietary configuration schemas, within the same user interface, cutting the learning process drastically and reducing the need for advanced simulation expertise and data modelling skills to achieve reliable and realistic results. Also the user interface enables end users to rapidly explore different design variants (not feasible when using current existing stand-alone simulation tools) that are evaluated simultaneously in the ISES cloud. This way, many design variables and design alternatives, e.g. window types, roof and façade solutions, as well as different space occupancy, may be explored in order to facilitate evaluation and selection of building components and space utilization in optimizing the building energy performance.

To prepare a holistic building model ready for use in an energy simulation tool, a large amount of precision data needs to be assembled. The normal BIM model prepared by the architects and engineers does not include all the necessary data. To overcome the CAD/BIM limitations to model complete energy related information the ISES project adopts the HESMOS Multi-Model Framework concept for interoperability of multi-model data, but extends it using semantic technologies, which also provides the basis for automatic rule-based inspection, correction and optimization of the different multi-model domain data and its interlinking. The ISES Multi-Model approach is an instrument that enables the entire relevant data domain, e.g. geometric model, space definitions, material data, occupancy information and weather data, although using different data models, to be linked together as a single source and thereby ensure a homogeneous view on all energy relevant


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information for the generation of the simulation building model. This is an innovation allowing highly streamlined processes in the design decision workflow.

Besides the BIM data there is a great deal of energy specific information that is defined in building simulation models such as thermal properties of building materials, energy loads stemming from building occupancy (presence, activity and behaviour) and local weather data that end users must provide to complete the simulation model. The required data exists, but in distributed, heterogonous data sources that require substantial manual preparation for use, which often becomes the source of inaccurate and incomplete simulation models. To overcome this issue, ISES provides a Simulation Resource Framework and a service platform, facilitating access to energy-related resource libraries and design decision templates that enable re-use and sharing of this data by energy simulation tools in tandem, consistent and uniform manner. As a result, end-users may view this heterogonous data as if it was a single data source. The framework itself does not store any of the data, so at any time, end users always access the latest information from the original data source, e.g. manufacturers catalogues. The framework along with the ISES multi-model platform ontology further allow end users to focus on the analysis and problem-solving issues rather than on the acquisition and transformation of the required data, thus minimizing the risk of inaccurate and inconsistent building simulation models.

Building energy simulation tools produce a vast amount of detailed output data that can be a challenge for non-energy or simulation experts to comprehend in a meaningful way. ISES provides tools that filter, organize and reduce this output data to a set of energy Key Performance Indicators (eKPIs). These eKPIs along with developed visualization tools help end users get a comprehensive view of these otherwise extensive and complex output data sets and allows them to transparently evaluate and decide on different design solutions, component selections and different usage scenarios of the building.

Whole building energy simulation is based on multitude of design variables that contribute and influence the energy efficiency of a building in different ways and alternatively vary with different design solutions and building types. Analysing and synthesizing this large variable space and their influence on energy efficiency requires a substantial effort that exceeds the capability and what is feasible with current stand-alone energy simulation tools in practise.

ISES developments enable end users to sort through hundreds and even thousands of design variants (building materials, occupancy and climate variations) in a relatively straightforward way, by (1) the high performance computing services and (2) multivariate eKPI analysis and decision support using integrated sensitivity and visualization services when determining, for example, which are the most influential design variables for a given building design or which is the best suitable target design solution derived from a possible solution space. Thereby, one can greatly improve the quality and efficiency of the design solutions.

The existing ISES infrastructure demonstrates software interoperability and facilitates future integration of new tools and necessary data. Parametric studies become easier to perform and are significantly expedited through parallel simulations using cloud technology. This way, users can examine a plethora of different building envelope component materials (e. g. façade elements, windows), electromechanical systems, and various scenarios of occupancy and weather conditions, for different types of spaces and buildings, from the conceptual building design stages to construction, operation and refurbishment.

The following sections provide practical examples and case studies of how to use and benefit from the ISES IVEL. At the end, a SWOT analysis outlines the main advantages/disadvantages and summarizes relevant opportunities/threats.




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2 Energy Efficient Building Design with ISES

The ISES process facilitates simulation, assessment and optimization of building energy performance

in variations of real life scenarios, acknowledging the stochastic nature of some input parameters.

The IVEL platform facilitates a holistic analysis of the building in order to make informed design

decisions. The functional structure includes several tiers. The domain modelling and input combine

several tools and databases for modelling the building, its envelope components, climate and

occupancy. A multi-model combiner and simulation configurator integrated into an nD Navigator

uses an easy to use graphical interface to automatically combine different models to meet the user

requirements.

The information framework of the IVEL is based on an integrating platform ontology binding together

the model of the facility represented as a standard BIM / IFC (Industry Foundation Classes) model

and the multi-model environment of related external information resources, such as stochastic data

(e.g. material properties, occupancy profiles and climate/weather information) and manufacturer

product components provided in digital catalogues (Figure 1). The platform combines several types

of services and applications, bounded together by a common Core Module that acts as the

middleware providing the required data and functional interoperability. All other components are

consistent with the identified use cases and can easily be extended or re-configured in accordance to

specific preferences and building types.

Figure 1: Schematic of the ISES IVEL.

The end user interacts through the multi-model nD Navigator to handle all processes and facilitate all necessary functionalities through various modules.




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The main features include the:

1. Design Module that integrates a BIM-based CAD system, a product catalogue module for the

selection and testing of new products and supporting tools capable to produce and export IFC

model data.

2. Requirement Management Module that integrates a facility management system and related

energy and cost estimate tools.

3. Common Access Module that provides a general-purpose interface to the IVEL via a web

application and facilitates several investigations on life-cycle building energy performance.

4. Cloud Service Module that provides energy related analysis and simulation services and tools, a

simulation model configurator for simultaneously assessing alternative scenarios using

stochastic values and distributed information resources (product data catalogues, climate

databases, BIM data etc.) and finally, reporting tools for the generation of aggregated results for

decision makers.

The following chapter demonstrates the ISES process and the use of the various modules, from

problem building definition on the IVEL, the available online tools and closing with an overview of the

results.




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3 Using the ISES Virtual Lab Platform

3.1 Overall Approach

The use of standardized energy-related Key Performance Indicators (eKPIs) can provide valuable insight for improving a building’s energy design and operation (Balaras et al 2013). These are simple numeric metrics that are easily associated with a building’s energy performance (i.e. lower or higher energy use) as a result of the building’s characteristics, design, equipment selection and overall operation.

Starting with early building design, eKPIs can be used as benchmarks in order to assess and quantify different design choices or other scenarios and even against other buildings or historical performance. Common in routine building design practice, the first step is to calculate power demand (loads) or energy demand in an effort to minimize system sizing (to meet building codes or minimize first cost). Other indicators may also be used for the assessment of indoor thermal comfort conditions under free floating conditions (e.g. minimum and maximum indoor temperature), and indoor air quality (e.g. different air flow rates and minimum fresh (outdoor) requirements).

The final (site) energy breakdown of different fuels (e.g. renewables, electricity, heating oil, natural gas) and primary (source) energy consumption, can also be used to facilitate the assessment of environmental impact (e.g. emissions). Although different times steps may be used (e.g. monthly), the most common is on an annual basis (e.g. annual energy consumption or annual emissions). In addition, eKPIs can be used for evaluating different scenarios for equipment and system selection that can lower the total building’s energy consumption, specific end-use energy consumption, e.g. related to heating, cooling, ventilation (HVAC) equipment, lighting, service hot water (SHW).

Depending on priorities, different indicators can be used to support the decision making process. For example, in order to facilitate the comparison between different size buildings or set priorities among different size zones in a building, values are usually normalized per unit floor area, e.g. power (kW/m2), energy demand or consumption (kWh/m2), and sometimes per unit volume. Similarly, energy indicators may also be normalized for different weather conditions or variations from year-to-year, e.g. using heating degree days for heating (kWh/m2.HDD).

Relevant economic indicators (e.g. taking into account first cost, energy cost or savings) for the life cycle of the building and components, are also taken into account to support the final decision making process.

To facilitate the use of the various eKPIs in ISES, the results are grouped and assigned to

specific sustainability dimensions.

All the results are accessible for further analysis within the ISES nD Navigator which is the

primary engineering tool used to access the platform and its numerous backend services

facilitating the energy-efficient design process.

The ISES approach is based on the federated use of multiple independent model data schemata with IFC as the main underlying schema. The elements of the other specialized schemata (e.g. climate data, material data, occupancy schedules, CFD Simulation data etc.) are linked to components of the IFC data schema using the link model approach (Protopsaltis et al. 2014c). Within the Multi Model Combiner (MMC), the data of the various applications are treated as independent information resources with their own application domain, data schema and data formalism. An example is illustrated in Figure 2.

The various processes are executed in the background without intervention or effort by the user.




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Multi Model Combiner (MMC)

MMC Activity MMC Select BIM/IFC Model MMC Select Spaces

MMC Material Parameters

MMC Climate Data o Perform sensitivity analysis

o Define Target Value Areas for Energy Analysis

o Perform Energy Analysis with

Energy Solvers MMC Load detail climate data

MMC Load Material Parameters for CFD Energy Solver

o Perform CFD Analysis

o Present Final Results

Figure 2: Multi Model Combiner interactions for coupling indoor flow and outdoor microclimate flow.




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Using the performed real project studies, the following sections elaborate the available ISES features

and tools and the results that can be obtained by performing detailed BEPS with the ISES platform.

3.2 The Pilot Buildings

Two real projects have been prepared as pilot demonstrators for the ISES process and IVEL to enable evaluation of the developed platform and its performance and applicability in a practical industry environment.

The first is the “Mavrica” Kindergarten in Trebnje, Slovenia (Figure 3 - 7), which was used to verify

IVEL performance in the energy efficient design using the advanced façade elements of the Qbiss

Air System developed by one of the ISES partners (Trimo). The building consists of 14 playrooms,

administration rooms, kitchen, laundry, ironing room, and a boiler room in the basement. The

building facilitates about 250 children. This pilot project was also used to show how ISES helps

component / building element providers to integrate existing elements in the BIM and the subse-

quent energy analyses and how the development of new such components is facilitated by ISES.

The second is the “Junge Oper” the Young Opera House in Dresden, Germany. It was chosen as

validation scenario for the refurbishment/retrofitting process. The project is currently being

executed with one of the ISES partners (LAP) as designer. It addresses an auxiliary building of the

widely known Semperoper (Figure 8). The building has a quadratic footprint of 25m, a basement,

three over ground floors and an attic. The work plan intends to preserve the façade (Figure 9)

and implement extensive interior changes. The ground floor will provide more open space for a

foyer and a café. On the 2nd and 3rd floor, a small stage for rehearsals and young performers will

be created (Figure 10). To analyse the effect of the refurbishment and the change of its usage,

two models of the building are generated: one for the existing structure and another one after

the refurbishment (Figure 11).

The main architectural and E/M characteristics of the two pilot buildings are summarized next.

Pilot building No 1: “Mavrica” Kindergarten, Trebnje, Slovenia

Figure 3: Mavrica Kindergarten - Main building facade.




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Figure 4: Mavrica Kindergarten - Façade details

Figure 5: Mavrica Kindergarten - Building interior

Figure 6: Mavrica Kindergarten - Floor plan of the ground floor.




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“Mavrica” Kindergarten Basic Parameters

Type of building Kindergarten

Location Trebnje, Slovenia

Construction period 2010 - 2011

Gross floor area 3,050 m²

Gross volume 12,200 m³

Rooms 103

Typical usage Playrooms / administration offices

Facade Curtain wall

HVAC Biomass heating; compressor cooling; mechanical ventilation with heat recovery

Figure 7: Overview of the main architectural and E/M characteristics of the “Mavrica” Kindergarten

Pilot building No 2: Young Opera, Dresden, Germany

Figure 8: Existing auxiliary building of the Semper Oper, Dresden




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Figure 9: Detail of the facade that will be preserved during the refurbishment

Figure 10: The Young Opera after refurbishment with its stage room on the 2nd and 3rd floor




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Young Opera Basic Parameters Before Refurbishment

Type of building Auxiliary building of an opera house

Location Dresden, Germany

Construction period 1983-1985

Gross floor area 2,660m²

Gross volume 9,700m³

Rooms 80

Typical usage Cantina / offices / workshops

Facade Curtain wall

HVAC District heating, no cooling

Young Opera Basic Parameters After Refurbishment

Type of building Theatre

Location Dresden, Germany

Refurbishment period 2014-2015

Gross floor area 2,480m²

Gross volume 9,700m³

Rooms 22

Typical usage Theatre / restaurant

Facade Curtain wall

HVAC District heating, forced-air cooling and ventilation

Figure 11: Overview of the main architectural and E/M characteristics of the Young Opera

3.3 IT Environment

The ISES Virtual Energy Laboratory follows functional requirements defined in (Jung et al. 2012)

where relevant end‐user activities were investigated and linked to the exchange requirements of the

defined use cases.

While the end-user interface (i.e. the nD Navigator) is accessible via a standard Web Browser

such as Internet Explorer, Firefox or Google Chrome, all of the calculations introduced

through various iteration cycles are performed using a high-throughput computational

environment (HTC) hidden behind the developed ISES cloud technology (see Figure 12). This

results in a flexible high performance system that can run any required software as needed

with the desired or available amount of processing power. However, performance in certain

specific cases might be not as good as expected because the chosen configuration favours

flexibility over performance. Flexibility is an important issue especially when use of the IT

environment by SMEs is concerned. These are most design consultancies in Europe in the

current practice.




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Figure 12: ISES cloud test-bed architecture

3.3.1 Hardware / Cloud

The computing resources and the ISES Cloud API are running on ISES Cloud test-bed with the

following hardware specification (Figure 13):

10 Intel Xeon Processor L5520 (2.26 GHz), 1U

8MB shared L3 cache 8GB (2 x 4GB)

PC3-10600E (UDIMM) NC362i

Integrated Dual Port Gigabit

Embedded SATA RAID 0, 1, 10

HP 2 x 500GB 7.2k Hot Plug SATA

Fiber-Channel disk array – 5 TB

Figure 13: ISES cloud test-bed located at University of Ljubljana




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The deployed dedicated hardware runs the latest long-term support Ubuntu 14.04 Linux

operating system. The test-bed can also integrate with non-dedicated hardware units that

run Windows 7 operating system. The deployed cloud infrastructure is based on OpenStack

cloud environment.

The high-throughput computing (HTC) environment deployed on the ISES Cloud is based on

HTCondor distributed resource management system. HTC environment is used throughout

the use of cloud-based resources.

HTC clusters can be quite expensive to implement, operate and maintain since common approach for

HTC is to build it out of a low cost computers and servers. The problem of such approach is twofold:

• Relatively high degree of technical expertise is needed for the setup of the HTC

environment and the operational support.

• Hardware depreciation in terms of computing power as well as maintenance costs due

to the limited-time availability of spare parts results in a slow degradation in the

throughput power and speed of such system.

Accordingly, the ISES approach is once again hybrid, allowing the backend environment to

include any number of low-cost and/or high-end computers/servers in order to upgrade and

extend the performance of the ISES Cloud services.

ISES Cloud API

End-users interact with the ISES VEL through the nD Navigator, which represents the entry point to

the use of the ISES Cloud. The whole communication between nD Navigator and ISES Cloud software

is exclusively through the ISES Cloud API while the complexity and technicality of the whole process is

completely hidden.

This means that the user does not have to bother at all about the technical infrastructure

but can concentrate entirely on her/his actual design tasks while benefiting from the use of

the high-performance cloud environment enabling faster and more detailed analyses and

examination of a broad range of alternatives which would otherwise hardly be possible.

3.3.2 Software Tools

The IVEL provides direct access and facilitates the use of various services and tools. Currently available

tools are for singe-zone, multi-zone and full building energy simulation, sensitivity analysis, CFD

analysis, requirements management, definition and management of energy resources via uniform data

templates as well as post-processing, visualisation and navigation utilities for fast and well-grounded

decision making. To handle that broad scope efficiently, a multi-layer multi-model approach was

implemented. The tools are also elaborated in more detail in (Protopsaltis et al. 2014a).

NANDRAD (www.bauklimatik-dresden.de/nandrad/) is a solver kernel for multi‐zone building energy

performance simulation. It is designed to perform transient solutions to energy balances in thermal

zones and discretized construction elements. It provides also thermal comfort evaluation (e.g. indoor

air temperature, operative room temperature, temperature of the inner surface of envelope

elements). The solver is applicable to buildings with a large number of spaces using optimized

numerical algorithms.

The tool may be used for passive and full building thermal simulations.


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THERAKLES (http://bauklimatik-dresden.de/therakles/index.html) is an energy simulation tool that

calculates thermal transport processes in individual thermal zones in buildings. The simulation takes

into account the effect of the outdoor climate, the usage characteristics, the particular features of

the ventilation, cooling and heating as well as the influence of neighboring zones. The lightweight

engine is designed to calculate results very quickly.

The tool may be used to perform multiple parallel sensitivity simulations as precursor and

background for informed energy-aware decision-making and selection of alternatives for

further sophisticated full building simulations.

RIUSKA (http://www.granlund.fi/en/software/riuska/) is a versatile thermal comfort and energy

simulation application. It uses as input an IFC model to calculate the thermal conditions of a building

and its spaces in different loading and weather conditions. Built upon the DOE-2 energy simulation

engine, it can be used to ensure compliance with the user requirements and objectives, comparison

of architectural solutions (window protection, façade solutions etc.), analysis of problematic spaces,

projected consumption of maintenance etc.

The tool may be used for early design studies where fast performance and versatility are

more important than full modelling and computational accuracy. The application can be

used for the following tasks: ensuring compliance with the objectives; estimating indoor

temperatures; comparing indoor environmental conditions; comparing architectural

solutions (e.g. windows, shading, façade solutions); comparing and dimensioning of

systems; identifying and analyzing problematic indoor spaces; estimating the energy

consumption of the building and building systems.

SARA is a Computational Fluid Dynamics CFD tool for the simulation of 3D, unsteady, turbulent,

incompressible flows with energy equations and buoyancy effects. It is an edge-based implicit finite

volume method software, employing flow variables on 3D unstructured hybrid polyhedral meshes. Its

features include various turbulence models for the simulation of the particular characteristics of each

flow field and its accuracy has been verified by excessive studies of well documented benchmarks

from the literature.

The tool may be used for visualizing indoor air flow patterns, using an automatic mesh

generation, various possibilities for the definition of complex boundary conditions, easy

wind definition and sophisticated graphical presentation features.

3.4 Preparing Energy Simulations with the ISES nD Navigator

The preparation of the input data for energy simulations is handled by the nD Navigator of the IVEL.

The purpose of this graphical user interface is the BIM management (Figure 14), the simulation

management and the management of energy resources, e.g. assignment of materials to building

elements or assignment of climate to the building site. The nD Navigator is elaborated in D6.3 (Laine

et al. 2014a).


http://www.granlund.fi/en/software/riuska/



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Figure 14: BIM management in the nD Navigator

The nD Navigator is a common platform for all users. It can be used to:

Upload IFC files and assign climate data, material data and occupancy data to their entities;

Visualize semantic relations within the BIM, for example, the façade elements can be viewed

or the structure of a room or building storey (Figure 15);

Provide feedback about user assignments and selections in a 3D viewer;

Manage simulations in the cloud. It is possible to perform passive simulations, full building

energy simulations and CFD simulations, and sensitivity analysis. The status of each

simulation is given and the input and output data can be downloaded. Variations can be

created, e.g. to change the thickness of material layers, which are also supported by

stochastic algorithms;

Visualize simulations results.

Figure 15: Check of U-values of assigned materials in the ontology (green: ok, red: critical)




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As already mentioned, besides the BIM data, there is a large amount of additionally needed energy

specific information that is not directly available in BIM, but is needed to complete the simulation

model. To manage integration of this inhomogeneous data, the nD Navigator manages selection and

assignment of resources to individual IFC model elements, e.g. façade, spaces and site elements, by

using the IVEL Resource Management Services and the Simulation Resource Framework (SRF).

The SRF is a restful web service platform supporting the integration of BIM with various non-BIM

energy-related resources and energy domain data, hosted in distributed networked data sources e.g.

file systems, web services and data bases in heterogeneous data formats. It also provides any

necessary data translations (e.g. data transformations, mapping and validation) to a homogenous

data format, formalizing the exchange requirements used by the ISES integration services. Thereby,

the SRF facilitates the coherent sharing and re-use of domain data by different energy simulation

tools in tandem.

The SRF provides various design decision templates and resource catalogues to fill in the gap and

underpinning the process of enhancing the IFC model to building energy performance simulation

(BEPS) specification to be effectively used in downstream BIM based energy simulation and analyses

applications (Gudnason et al. 2014). Various energy domain resources are available in the resource

framework, using energy-enhancement templates (eeTemplates), such as:

Schedule Databases

Constructions & Material Databases

Façade elements Catalogue

Weather/Climate Databases.

Figure 16: Simulation Resource Framework

Currently, the following information resources are integrated and are available on the nD Navigator.




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Constructions & Material Databases

Building assembly catalogues for building envelopes (e.g. walls, floors, roofs and slabs on grade) for performing simulations that need a full set of thermal properties for each material layer. Over 650 different multi-layered assemblies with respective material references are currently available (Figure 17).

Material catalogues that contain standard material types with detailed thermal properties (e.g. conductivity, thermal capacity, density, thermal resistance) for RIUSKA and NANRAD simulation engines. Over 1100 different materials with detailed energy specific properties are currently available. In addition, variations of different material properties can also be defined (Figure 18).

Product catalogue for commercially available façade elements (“Qbiss” catalogue).

Stochastic Templates

Design eeTemplates are well-verified patterns proved and used in practice and research projects. They include deterministic values and parameters representing the average of a series measured physical values. In ISES eeTemplates are extended to include also distribu-tions of parameters with uncertainty. Naturally, almost every measured value has a fluctuation within a particular range. This requires enrichment of the parameter space to account for such variations. For eeTemplates describing materials the characterization of, e.g. the thermal conductivity (λ) of an insulation material with an average value of 0.035 W/(m.K) can have a negative and positive deviation of 0.005 W/(m.K). In order to map this distribution in a stochastic sampling process theoretically a very large number of simulation runs would be necessary. A meaningful step size of parameter adaption is therefore needed to limit the computations. eeTemplates regarding construction compositions can e.g. contain variations of thicknesses in one or more material layers. To find a sufficient performance of the building or zone from thermal energy consumption point of view, a step-by-step increase of the insulation layers provides a convenient way to identify the sought composition. ISES provides services enabling the necessary processing.

Figure 17: Assigning constructions (e.g. Qbiss elements) from the resource library in the nD Navigator




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Figure 18: Variation parameters within the nD Navigator, e.g. varying material layer thickness

Energy Loads and Spatiotemporal Schedule Databases

Occupancy and various operational energy loads and related spatiotemporal schedules are

included such as standard continuous annual schedules for occupancy, operation of HVAC

systems, lighting and equipment (e.g. office, elevators, kitchen) etc.


Similar to the eeTemplates of construction compositions and materials, there are schedule

data sets that provide well-verified deterministic patterns proved and used in practice and

research projects. However, occupancy and user interactions are among the most uncertain

inputs in thermal building simulation. A schedule defines the presence of people in a

particular zone which has substantial influence on the thermal behaviour. A user present in a

certain zone can control equipment or HVAC system components. Also, heat dissipation from

occupants has an effect on internal heat gains and the overall heat balance. Because of the

heavily uncertain nature of occupancy in reality it is important to consider this uncertainty in

thermal building simulations. With the created stochastic occupancy profiles using the

eeTemplates for schedules, a more realistic prediction of energy consumption can be

realized. In addition, it is also possible to improve the settings for HVAC systems because

with the variation of occupant densities, a simulation can reveal potential weaknesses in the

thermal comfort in a particular zone of interest.




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Weather/Climate Database

The database defines a Climate Zone table specifying location parameters, e.g. weather

station, longitude, latitude, etc. A Climate File table with typical metrological reference files

for Finland, Germany, Greece and Iceland is currently implemented as well as a Climate Data

table with a total of 42 parameters (temperature, humidity, wind speed, solar radiation etc.)

containing weather data for Athens and Reykjavik to extract various detailed weather

patterns for these locations.


Generating synthetic data representative of future conditions can be facilitated through an

assessment of previous extreme conditions. This analysis is supported by the ISES Weather &

Climatic Data Tool application, to process and gain an insight of available long records of

weather data compared against a test reference year, if available, or any other typical year.

The results illustrate the most likely high or low values of different weather data with respect

to a specific parameter (temperature, wind, cloudiness, moisture, pressure, etc.) and

reasonable ranges. Using this insight, one can then filter specific parameters for extreme

conditions (e.g. coldest, warmest, windiest conditions), and generate synthetic weather

patterns.

Platform Ontology

Definitions of relations between energy resources and IFC entities are supported in the

platform ontology framework. The ontologies are described through the Web Ontology

Language (OWL) and provide relations through object properties and data type properties

from BIM elements to non-BIM elements. The user assignments in the nD Navigator are

stored in triple stores so that the user can save current work for future use. The ontology

framework is elaborated in the ISES Deliverable D3.1 (Kadolsky et al. 2014). Rules can be

applied to check, for example, the range of U-values for the assigned materials (Figure 19).

This allows the introspection of critical elements regarding the thermal behaviour.

Figure 19: Available rule sets to check value ranges of assigned parameters within the nD Navigator




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3.5 Delivering Results with ISES

Building design needs sufficient feedback at each decision making step. A large number of

alternatives should thereby be produced and analysed to find the optimal solution(s). However,

there are two significant limiting factors for the efficient evaluation of such design alternatives: the

huge simulation time effort needed, and the difficulties to analyse the extensive amount of output

data (Laine et al 2014b).

The approach minimizes simulation effort via a multi-step procedure and facilitates the

assessment of simulation results via multi-eKPI analysis.

Simulations are performed on three levels:

Passive evaluation to detect critical zones,

Zone level assessment to detect most influencing parameters

Multi-zone (building level) evaluation to identify promising design alternatives for the entire

building.

3.5.1 Passive Simulations

The detection of critical zones in smaller buildings can be done manually by experienced energy

experts. However, when the number of zones increases, the critical (underperforming) zones can be

distributed over several storeys or on opposite ends of the building which makes it impossible to

predict without appropriate tool support.

To identify an exemplary list of the weakest zones of a large building, Passive Simulations can be

used. In this step, the main goal is to analyse the behaviour of the building on environment effects

without any activated HVAC system or user behaviour. An HVAC-system should support observance

of thermal comfort in extreme weather conditions but not be activated permanently. To guarantee

an excellent building performance the reactions of weather influences should not have a significant

effect. The more stable and closer to comfortable conditions the resulting indoor climate is, the

lower the energy consumption for heating, cooling, and ventilation (Laine et al 2014b).

Furthermore, as the computational resources for multi-zone simulations also increase with activated

HVAC systems, the simulation time for a Passive Simulation is significantly reduced.

Passive Simulation is an annual multi-zone simulation of the entire building with a typical climate

data set without extreme summer or winter periods. After a completed simulation run the first step

is the evaluation of all zones via eKPIs of the resulting operative temperature. The selected eKPIs for

the Passive Simulation consist of:

Lowest operative temperature

Highest quartile range

Highest whisker range

Highest operative temperature

Highest cumulated heat flux (gains of construction heat conduction gains)

Highest cumulated heat flux (gains of window short wave radiation gains)

Highest cumulated heat flux (losses of construction heat conduction gains)

Lowest quartile range




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The significance of an overall analysis is to find critical zones in the building and define groups of

similar zones. In many building types, whether residential or office, many zones only have slight

differences in the composition. Therefore, parameter adaptions can be evaluated for exemplary

zones of interest.

Result files of annual multi-zone simulation can contain several hundred Mbytes. Appropriate

processing and reduction to a list of zones of interest with statistic key values of resulting operative

zone temperatures will help the end-user to find a starting point for further working steps. Such

zones of interest are only suggestions for the user and do not bring in any constraints for the

following steps. Besides a textual description of the eKPIs and the list of zones of interest graphical

workups are created after analysing the passive simulation results. As an example, Figure 20 shows

the zones of interest, marked by red colour, in the simulated Mavrica Kindergarten, and Figure 21

provides the simulation results along with the eKPIs and the identified zones of interest.

Figure 20: Graphical presentation of Passive Simulation of Mavrica Kindergarten

Figure 21: Simulation results of the passive simulation (e.g. identify zones which need improvement)




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3.5.2 Sensitivity Simulations

Simulation results can be influenced by many input variables, such as the external factors (e.g.

weather, location), building layout and geometry parameters (e.g. orientation, shape, size, volume,

window/wall ratios), material-specific parameters (e.g. material type, U-value, etc.) and energy-

specific parameters (e.g. type of heating system, pumps, etc.). Investigations using Monte Carlo

Simulations (MCS) and sensitivity analysis have revealed some relationships between input variables

and eKPIs in order to address the contribution of each of these inputs to the output variables

(Christodoulou et al. 2014). The general concept is illustrated in Figure 22 for evaluating two metrics,

namely (1) the building’s total annual energy consumption and (2) the building energy rating (BER).

Figure 22: General concept of Monte-Carlo simulation and sensitivity analysis of various factors related to input data (Christodoulou et al. 2014)

The stochastic approach in ISES addresses the three main categories climate profiles, usage

profiles and energy related building material properties. Together, they form the whole

parameter space. Classical deterministic simulation models are used treating the input

parameter space as a separate stochastic model defining the building physical properties as

well as the initial and boundary conditions of the buildings. The arising complexity requires

to clarify which input variables are contributing significantly to the output uncertainty in

that high-dimensional models, rather than exactly quantifying sensitivity (i.e. in terms of

variances). This is the objective of the used screening methods. The approach targets

achievement of relatively low computational costs when compared to other approaches,

and can be used in preliminary early design analyses to weed out uninfluential variables

before applying a more informative and sophisticated analysis to the remaining set.

ISES supports a local or so called one at a time (OAT) approach which means the variation of one

single parameter per simulation. This appears at first as the logical way because any change observed

in the output will unambiguously be due to the single variable changed in the input. However, this

approach does not fully explore the input space, since it does not take into account the simultaneous

variation of input variables. This means that the OAT approach cannot detect the presence of

interactions between input variables. For that reason ISES enables also a global approach so that the

user can change various input parameters at the same time to analyse the interaction effect of those


http://en.wikipedia.org/wiki/Interaction_%28statistics%29



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parameters regarding the eKPIs. In the ISES IVEL it is possible to easily combine both stochastic input

(e.g. occupancy schedules) with manually selected deterministic parameters. This is done by using

the Simulation Matrix described below.

Thus, ISES provides a platform in which different existing simulation software can be integrated to

run in a cloud based environment. Currently, the NANDRAD energy solver is used in a passive

simulation step to identify at first those zones of the building which are most appropriate for the

following sensitivity analysis. Subsequently the THERAKLES Solver is used with these predefined

zones of interest to identify the influence of the chosen input parameters on the defined eKPIs,

enabling the decision maker to decide which parameters should be used in the following full building

simulations. The main part of the decision making phase is an interactive and integrated visualization

environment which consists of different user-driven ways to visualize main results from different

KPIs. This provides a more intuitive way to explore the model behavior complementing the

mathematical and statistical methods.

Regarding this simulation intent several data types have to be applied to the simulation engine. All

simulation cases are entirely described in the Simulation Matrix explained below, which is used by

the energy solvers to generate the appropriate input. Generating the Simulation Matrix automatically

is an essential step of the simulation workflow in the IVEL. Thus, ISES enables product manufacturers,

architects, HVAC designers and energy experts to take into account probabilistic input values and

semi-stochastic computational methods in comprehensive simulations of energy efficiency and

evaluation of performance and comfort.

Simulation Matrix

The Simulation Matrix is a medium to hold parameter variants of external factors, building geometry or element descriptions for thermal single- or multi-zone simulations. Its main

purpose is the storage of fewer variants connected to a building or building elements and the correct use of those in combinations to manipulate an existing project with assigned element

descriptions (Freudenberg et al. 2014). The underlying XML schema describes four main parts

of the Simulation Matrix. The first part is a selection of single zones or storeys of the building and has to be interpreted as chosen sequence of single-zone simulations including the parameter combinations selected by the user. The second part holds all variables, which can be global building parameters down to parameters for separate building elements, e.g. construction compositions or window values. The third part provides for grouping of zones or

building elements with the same assigned properties. The last part presents the decided

combinations connecting the second and the third part. It defines the simulations that should

be run in parallel to examine various design alternatives reflected by the defined parameter variations in comparison to the original project settings for the building. A combination can hold from one up to an unlimited number of changes of a building parameter, called variant of a variable. Each variant in a combination may, but does not have to, be connected to an assigned group. If a variable is associated to an assignment group then all variants are

simultaneously imposed on all elements in that group.

The Simulation Matrix is used in the sensitivity simulation phase to transfer the information from the IVEL into the ISES cloud. The main purpose is to create single zone simulations of user-selected rooms or zones of interest, combined with parameter variations in order to find out the effect of these variations to the selected zone. Technically, this is done by a pre-processing script written in the Python language which generates automatically the Therakles input files for each required simulation run using the enhanced BIM data, the assigned eeTemplates and the information from the




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Simulation Matrix. After the generation of all Therakles input datasets the requested parallel simulations for all decided parameter combinations are run automatically on the ISES cloud. Upon their completion, a post-processing service is run. It has the purpose to reduce the size of the output which is transmitted back to the IVEL nD Navigator and to simplify the annual hourly values in the Therakles result files to selected eKPIs, which are much easier to interpret by the end users.

In summary, the generalised sensitivity simulation process in ISES is as follows (see Figure 23):

Parsing and interpretation of the Simulation Matrix

Calling the developed ResCon service to process the selected eeTemplates from SRF

Converting the eeTemplates of construction, material, climate or schedules to the required internal Therakles format

Creating the Therakles input datasets and copying the Therakles solver to the cloud

Running the requested parallel Therakles simulations

Running the post-processor to derive the eKPIs and return these to the IVEL.

This technical description of the process does not explain the advantage for the designers. Given that working time in the different design phases is strongly limited and strictly restricted by compu-tational costs, the designer needs a possibility to evaluate several variants already in the early design phases to improve the building performance. Therefore, without ISES an analysis of possible para-meter settings and building configurations cannot be performed without time and financial loss that cannot be afforded in most practical cases. With ISES, such possibilities do exist.

Figure 23: Generall workflow of Sensitivity Simulations in the ISES-cloud

The performed simulation studies have shown that some parameter variations will not have significant effect on the building performance. Hence, the sensitivity simulations can be used to check and define parameters which do have a significant effect on the building’s energy performance in a particular context and are worth for further consideration in a later more detailed phase.

2 3

4

1

5 6




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The Sensitivity Simulations are fast and save time in the working process. To evaluate one

parameter it only takes around 30 seconds. With the power of cloud computing systems the

simulation time of all variants increases only marginally due to the ability for parallel calculations.

After all simulations are terminated, the post-processing of the sensitivity simulation phase

simplifies the huge number of result files and combines the eKPIs of every simulation run

zone by zone. This allows an acceptable visualization of all variants in the IVEL in the next

decision making step which can include all involved professionals in the design in a

knotworking session using the newly developed ISES sensitivity analysis and post-processing

tool embedded in the nD Navigator thereby enabling a holistic design approach.

3.5.3 Post-Processing, Sensitivity Analysis & Decision Making

Depending on the aims of a project, the analysis of the results may be based on different parameters,

suitable to evaluate indoor environmental quality, energy performance, peak loads and other

building characteristics. These eKPIs can then be used to compare and filter design alternatives.

However, considering multiple eKPIs, the number of analysed alternatives can easily increase from a

few simulations to tenths of thousands of results, compared to the current situation. The challenge

then becomes to process and to exploit these simulation results for reaching the optimum solution(s)

based of the project priorities.

The decision making process is supported by powerful tools and functionalities for

displaying, filtering, comparing and correlating results.

Displaying Parameter Sensitivity Results

Sensitivity analysis is a robust method used to determine the significance of different variables on

simulation results expressed through different eKPIs. It can be divided into:

Local methods: The user defines a base case and alternatives are produced by varying each

variable one at a time. This way, the solution space is limited by the variable definitions and

the interactions of variables are not taken into account. Accordingly, one can consider the

effects for each variable independently.

Global methods. The user can utilize random sampling to produce a comprehensive group of

solutions that represent the entire solution space. Usually this implies that more simulations

are needed and that the method and indicators to represent the sensitivity variable are more

complex. However, with using this approach, the interaction effects of the variables on the

results are taken into account.

Display and analysis of results can be handled by both methods. For building energy

simulations, a random sampling method with a minimum of 80 samples is sufficient for the

sensitivity analysis. This is an essential part of the user-controlled energy simulation process

that aims to identify an energy efficient building design by producing a large number of

alternative building design solutions. With sensitivity analysis it is possible to rank the

defined variables and to find the ones that explain most of the variances (Figure 24).




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Figure 24: Visualization of sensitivity analysis results, for six variables (e.g. space infiltration, U-values of building elements, efficiency of heat recovery unit, building orientation)

and seven eKPIs (e.g. energy needs, cost)

Filtering Results

Possible design solutions can reach a very large number of simulation outputs. Thus, in order to

facilitate the user in processing the solution space it should also be possible to screen the available

data within project specific targets that would facilitate interpretation of the results.

Target filtering of selected eKPIs is defined by the user and can be tailored to meet specific

project constraints. The main effect of the filtering is to differentiate amongst the various

solutions and to focus on the most important ones. Accordingly, the user can easily select

the upper and lower bounds of the simulation results for specific eKPIs to narrow the

examined eKPI scope (Figure 25).




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Figure 25: Target filtering by limiting the upper and lower bounds of different eKPI values. The pie chart automatically shows the percentage of filtered solutions (e.g. 69 out of 500)

Analysing Results

Multi-eKPI analysis implies a decision making process that aims to meet the intended target values

and determine the different alternatives on the basis of the best possible solution(s). Given that BEPS

may produce a very large number of design solutions, the user needs powerful visualization tools

that can be easily customized in order to facilitate the decision making process.

Developed new visualization tools. They are easy to understand and enable intuitive

reasoning to analyse the simulation results, investigate the effects of the variables taken

into account for different eKPIs and assess the impact on different alternatives.

Graphical representation using advanced techniques. Results including different eKPI and

other building parameters are visualized using scatter diagrams for single eKPI analysis,

hyper-radial via parallel coordinate plots for multi-eKPI analysis and radar charts for fast

overview and comparison of results (see Figure 26).




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a) Scatter diagram of simulation results

b) Hyper-radial via parallel coordinate plots for filtering simulation results by eKPIs. Alternative(s) are selected by defi-ning the preferred area of the selected parameters from all results.

c) Radar chart of eKPI-footprint for selected simulations

Figure 26 (a-c): Visualization techniques for multi-eKPI analysis




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3.5.4 Variant Building Simulations

After the reduction of variants without significant influence for the building performance on zone

level, varying parameters on full building level can be done. For that purpose, the user can select

preferred variants of parameters (Figure 27) and create alternative design settings of the building to

find appealing solutions for the final decision.

Figure 27: Selection of variants in the IVEL

Again, a simulation matrix is used to transfer the information from the IVEL into the ISES cloud. After

the upload of the data to the cloud, an automated process is initiated which parses and analyses the

simulation matrix to create the entire input folders with all additional files needed for the thermal

building simulations using NANDRAD. The first step in that process is the creation of a copy of the

original folder structure because predominantly a combination in the simulation matrix does not

contain variants for all building elements and global building settings. After that the parameter

changes are done in the copied folder structure. The adaption of the data comprises simple changes

of material parameters or complex replacements of entire data sets, e.g. climate data set or

supplanted layers in construction compositions. All detailed information, except geometry, is linked

and placed in separate files in the NANDRAD folder structure. If an eeTemplate is not available,

because it was not used in the original project, ResCon is called to provide the missing eeTemplate

from SRF (see sect. 3.5.2). Then, the scope of the necessary adaption has to be analysed. Assignment

groups cause new linked files in the NANDRAD input because overwriting the original data would also

have an effect for the linked elements outside the assignment group. An adaption of individual

parameters not using assignment groups causes only alteration of the linked file.

After all combinations in the simulation matrix are interpreted and the respective input data sets are

created the NANDRAD full building simulations are run in parallel in the ISES cloud. In contrast to

single zone simulations in the sensitivity simulation phase, a multi-zone simulation with activated




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HVAC systems requires from several minutes up to several hours for an annual run depending on the

project’s size. Therefore, the power of a cloud-based environment is needed. This technology does

not reduce a single run of NANDRAD but reduces the simulation time of all alternatives to a little

more than the time for one in comparison to sequential processing (N:1 time reduction).

Once the simulations are completed, post-processing is also initiated automatically whereby a vast amount of results has to be parsed and analysed. To reduce the file size and complexity of annual hourly data sets of different categories, e.g. thermal fluxes through walls or net-energy consumption of the HVAC system, eKPIs for multi-zone simulations are created. These eKPIs are intended as support for final decision-making. All eKPIs for the different alternative simulations are listed in one result file which is returned to the IVEL. The eKPIs can be shown in the same graphical presentation as in the sensitivity analysis (see sect. 3.5.3).

Figure 28: Final decision-making for the Mavrica Kindergarten

Figure 29: Visulization of eKPIs after full-building simulations




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3.5.5 CFD Simulations

CFD simulation concerns the detailed analysis of the indoor climate in buildings for the evaluation of

the performance of the HVAC systems, the prediction of thermal comfort conditions and the design

of special purpose ventilation, heating and cooling systems according to the geometrical design of

the building (building orientation, openings, glazing surfaces etc.).

A CFD application stands as a complementary tool to the Energy Simulation applications. It is

the unique tool for energy simulation in case of buildings of special use (operation theatres,

clean rooms etc.) or of non-conventional geometries.

The main outcomes of the CFD simulation are:

Determination of thermal comfort conditions (thermal comfort map on the BIM model)

Estimation of U values

Evaluation of HVAC equipment performance

Optimization of the HVAC installation

Evaluation of the efficiency of the BACS control system design.

The CFD simulation provides the greatest possible accuracy among numerical tools and is the best

alternative to experimental measurements, which actually give the most realistic flow field data.

The main drawbacks of CFD simulations commonly involve

Huge efforts for the preparation of geometrical model data (e.g. input of the geometry from a

CAD system, simplification of the geometry for the definition of the “thermal envelope”,

assignment of domain boundaries), material properties for the air and the wall materials (e.g.

glazing surfaces, facades, concrete walls etc.), boundary conditions of the flow field, solar

radiation (as a result of local climatic data), wall temperatures taken from a BEPS model, air

inflow data that depend on the climatic data and the orientation of the building envelope,

cooling and thermal loads, HVAC systems etc.

Excessive computing costs due to the need for demanding computational meshes of millions of

elements/nodes for 3D turbulent flows.

To overcome these drawbacks, the thermal envelope is automatically extracted from the

nD Navigator and site and climatic data are obtained from the Resource Framework and

climatic databases through the Simulation Configurator so that the CFD computations

become affordable when carried out on the cloud.

The data required for the CFD analysis are summarized in the following table, showing also their

sources in the IVEL.




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CFD analysis data Data source in iVEL

Walls temperatures BEPS results

Walls materials’ data energy‐enhanced BIM input file

Climatic data (solar radiation) energy‐enhanced BIM input file

HVAC air flow rate and characteristics BEPS results

Inflow conditions of natural air or natural ventilation heat/loss gain

BEPS results or climatic data

Outflow conditions for air exhausts Climatic data

Occupancy data BEPS results

Turbulence model User defined

Time simulation User defined

Thermal loads (from equipment, due to occupancy) BEPS data, User defined

Various numerical parameters controlling convergence User defined (default values are given)

Radiation flux from inner walls BEPS results

The evaluation of the results according to thermal comfort criteria is based on eKPIs

oriented to thermal comfort that have been introduced in the context of ISES, e.g. Predicted

Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD), local thermal discomfort

indices like the percentage dissatisfied or draught rating risk, percentage dissatisfied due to

vertical temperature differences etc., as functions of local air velocity, temperature,

humidity, turbulence intensity and in close relation to the occupancy activities for each

specific space, and contaminant concentration indices for indoor air quality assessment.

The CFD code has been enhanced with post-processing tools for the representation of all the

above thermal comfort KPIs, in the form of thermal comfort maps on the buildings’ zones,

isosurfaces of the critical KPI values, isolines and isosurfaces in the occupied zones and in

the breathing zone and easy graphical representation of the flow field variables as well.

Data input, model generation and evaluation of the results for the case of cooling of the “Young

Opera” pilot building, by using displacement cooling/ventilation are depicted in Figure 30 - 32. The

required data are completed by using results of the BEPS tools of IVEL.

The CFD simulations illustrated in Figure 30 - 32 required about one day on 96 CPUs on the

IVEL cloud, whereas according to a rough estimation it would take at least one week on

16 CPUs.




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Data Input

Figure 30: Model preparation for CFD analysis in IVEL, “Young Opera” pilot building in Dresden

Evaluation of Results

Figure 31: Cooling of the “Young Opera” model by means of mechanical displacement cooling/ventilation. The figure shows the temperature isolines on a vertical plane

(the occupied zone is included)




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3D streamlines

Temperature isolines on the horizontal plane z=1

Temperature isolines and velocity vectors on

vertical planes, y=-2

Temperature isolines and velocity vectors on

vertical planes, y=0

Streamlines coloured with temperature

3D representation of thermal comfort indices (DR)

Figure 32: Data input, model generation and evaluation of the results for the case of cooling of the “Young Opera” pilot building, by using displacement cooling/ventilation




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3.6 Platform Performance Benchmarking

The situations and frontiers of when to use computer clouds are quite diverse and the answers to

this question can vary based on the anticipated usage workflow, available resources and technical

skills of the end-users.

The benchmarking is in most cases considered to be a process of identifying a point of comparison

against which the performance is compared. Due to the specific characteristics of the ISES Cloud and

its deployment, benchmarking of the developed ISES Cloud and related services should consider the

following objectives:

As industrial cloud deployments are still rare but with strong market perspectives, especially

in computing intensive engineering fields such as energy, there is only a partial vision of the

opportunities in this field. That is why the performance of the developed solutions has to be

measured in order to gather knowledge that will serve as a recommendation for similar cloud-

based ICT developments in the energy, AEC and other related domains.

ISES Cloud deployments occur using many different technologies and involving different

stakeholder mixes. That is why it is important to have metrics allowing for comparison of

different tools, services and solutions to learn by identification of good practices.

The results of the benchmarks should be critically analysed, compared and discussed

especially through the prism of the SMEs that constitute the majority of the stakeholders in

the fields targeted by the ISES project.

Accordingly, a number of tests were performed in order to benchmark the developed cloud

infrastructure allowing the stakeholders to plan, monitor and evaluate the characteristics,

performance and perspectives of the cloud based deployments.

The performance can be measured with the comparison against price, flexibility, usability, scalability

or any other quantitatively or qualitatively descriptive factor.

The performance and experiences with the IVEL and ISES Cloud were measured and

compared considering mainly the two factors time and price, dividing measured results and

experiences according to the relevant steps in the general ISES workflow.

3.6.1 BIM Conversion

Conversion from BIM to simulation data model is a process that is executed at the beginning of the

workflow and when the underlying building information model changes. Experiences and

measurements (Figure 33) showed that, although running a single job through a HTCondor

computing environment presents some overhead for data transfer to and back from a remote

computing resource as well as general HTCondor job management overhead, there are still valid

reasons to use HTCondor for running single jobs, e.g. better compute resource utilisation, local

computer free for other business tasks, etc.




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Figure 33: Prepare simulation data – convert IFC + RDF model information to Nandrad project

3.6.2 Passive Analysis

The passive analysis can be executed immediately after the BIM conversion step. As a process of

analysing a large building can take relatively long time it is even more important to execute the

process on a remote compute resource. Although this adds some time overhead and does not show

significant savings in terms of time (Figure 34) as the task cannot be parallelized, it provides other

benefits, such as leaving local computing sources intact so that the end user can focus on other

computer intensive tasks. Specifically, in the ISES context, another benefit is that the initial vast

amount of data required for the energy simulations is transferred only once to the cloud envi-

ronment within a concurrent background task so that for all following computations only little

additional data provided mainly via the simulation matrix is needed.

Figure 34: Passive analysis of a building

3.6.3 Sensitivity Analysis

Sensitivity analysis of the selected building was performed with two different applications, i.e. Riuska

and Therakles. Both applications were used to perform sensitivity analysis of a building to determine

critical zones but for different buildings. The benchmarks of the Riuska application were heavily

influenced by the use of the Wine environment to run the Windows native Riuska executable on the


http://www.granlund.fi/en/software/riuska

http://bauklimatik-dresden.de/therakles/index.html



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cloud. The Riuska application can perform 3-5 times faster when executed in the Windows operation

system and can thus be considered on par with the Therakles application.

In the case of the Therakles application which was re-compiled as Linux native application the

performance gains are considerable even if only 10 CPUs are utilised, e.g. 1000 cases calculated locally in

about 1h 15min compared to about only 20 min on 10 CPUs in the HTCondor pool (Figure 35). Maybe

even more illustrative is that if 100 CPUs in the HTCondor pool are used 1000 cases can be analysed

in less time than 100 cases can be analysed locally. It should also be noted that the performance

speed-up is not linear when utilising more CPUs as one might expect. The reason for this is of course

the input data transfer to a remote machine and the results data transfer back from the remote

machine as well as the job management of the HTCondor pool. Data transfer overhead could be

limited by using shared data store.

Figure 35: Sensitivity analysis of a building using Therakles application

3.6.4 Full Building Simulation

Next in the typical ISES workflow is the full building simulation. After the sensitivity analysis the end

user is supposed to select use cases that are most relevant for determining the overall energy

performance of the building and run full building simulations to obtain better insight of the expected

energy performance.

Figure 36 shows the graphical representation of benchmarking results for 1, 5 and 10 cases using

single local CPU and utilising HTCondor pool CPUs. It is clear that the overall performance is

dependent on the number of available CPUs. As the number of cases considered is small (5 - 10) the

overall simulation time usually equals the simulation time of a single full building simulation. In the

test case this was approximately 4 hours.




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Figure 36: Full building simulation using Nandrad application

3.6.5 CFD Analysis

The final step in the ISES workflow is running user defined CFD analysis that provides extra energy-

related design insight for the examined building:

3D detailed results in space and time for indoor climate

Thermal comfort prediction and Indoor air quality estimation

Evaluation of HVAC systems performance in terms of thermal comfort requirements

Coupled simulation of indoor and outdoor climate.

For benchmarking, the biggest room (performance stage) of the Young Opera pilot was selected for

CFD analysis. Following are some of the parameters of the CFD analysis:

Ventilation and cooling of a warm room is done by using roof vents

Mass flow rate 0.3 Kg/s at temperature 15°C

Initial room temperature set to 35°C

Walls are taken as adiabatic, windows are transparent to solar radiation

An unstructured numerical mesh of 453755 pyramids and 89476 nodes is used

The transient phenomenon is examined

Wall functions are used for the treatment of turbulence on the solid walls

The physical time step is 0.01-0.1 sec

Air properties ρ = 1.225Kg/m3, Cp = 1006 J/kg.K

Figure 37 illustrates some of the results obtained from the CFD analysis and visualised via the nD

Navigator.




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Figure 37: Stream lines colored with temperature and pressure

CFD analysis is a very performance demanding analysis requiring HPC environment with sufficient

available CPUs. In the context of ISES, CFD analysis using Sofistik SARA software was performed on

the same hardware as other simulations or parametric studies. The CFD analysis was performed with

two different configurations of the computational environment; once using 16 CPUs and once using

96 CPUs. Figure 38 shows the summary of the CFD runs. It is clear that the number of available CPUs

have very important role in speeding up the CFD analysis. It is also true that the speed-up is not

linear and that just adding more and more CPUs does not necessary mean any significant speed

improvements. Actually, the overall performance of the CFD analysis is dependent on a number of

factors, for example, generated mesh, implementation of the Massage Passing Interface libraries,

type of connections between hardware computing resources, etc.

Figure 38: CFD analysis using Sofistik SARA application




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3.6.6 Overall Performance

Among the strengths of the proposed cloud approach are:

The scalability of the ISES Cloud with the ability to add new resources on short notice within

the limited costs

Simplicity of the developed cloud based workflow, hiding the complexity of the backend

infrastructure

Centralized maintenance with single-point update infrastructure

The ability to access data and applications anywhere, anytime

Platform independence (ability to work on any operating system)

Last but not least, the ability to parallelize independent tasks.

Performances of the developed and presented ISES Cloud have indicated significant potential for

usage in everyday engineering production environment. Nevertheless, it has been observed that the

performance of the developed ISES Cloud varies from task to task. As it was shown, for every

application deployed to the ISES Cloud environment there is a tipping point where added value of the

cloud environment starts building up. Below that point, performance may be even deteriorated

compared to the use on a single computer. The greatest benefit is in:

(1) The ability to run a huge number of parallel computations for the time of a little more than

one, which makes sensitivity simulations possible for practice

(2) The ability to run sophisticated analyses at affordable conditions thereby enabling more

informed and better grounded design solution.




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4 SWOT Analysis

This section explores the main strengths and weaknesses of the ISES process and the IVEL from a

technical point of view, and identifies the opportunities and threats that emerge as a result using the

SWOT analysis approach (Lombriser & Abplanalp, 2010).

A SWOT analysis is a structured planning method used to evaluate the strengths, weaknesses,

opportunities and threats involved in a project or in a business venture (Figure 39). It involves

identifying the internal and external factors that are favourable and unfavourable to achieve a long-

term objective.

This includes the analysis of internal and external factors as follows:

The internal factors address:

Strengths - characteristics that give advantages over other approaches

Weaknesses - characteristics that show disadvantages in comparison to other related efforts.

The external factors address:

Opportunities - elements of the environment that could be advantageously exploited

Threats - elements in the environment that could negatively impact the success of ISES.

Figure 39: Schematic presentation of the SWOT analysis approach

4.1 Strengths

Open Software

The IVEL (Figure 40) has a flexible software architecture based on the service-oriented paradigm and

comprising several types of services and applications, bound together by a common open source

Core Module that acts as the middleware providing the required data and functional interoperability.

All other components can be extended, replaced or re-configured in accordance to specific

preferences and building types.




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Figure 40: Overview of the ISES platform

Off-the-shelve applications with additional plug-in services provide the bulk of initial design

information in the form of BIM (IFC2x3) data to the platform (CAD, FM system). Services are available

to enhance the BIM data to an energy-extended BIM framework (Climate/Weather tool, Product

Catalogue tool, Resource Service Framework enabling the use of various resource catalogues and

stochastic data).

Flexible BIM-based integration of all needed energy-related data

External information resources are integrated in uniform manner through the simulation resource

service framework which provides capabilities to define templates for the description, processing

and management of various needed non-BIM data, such as climate data, material data, product

component catalogues, occupancy / activity schedules, as well as stochastic parameter variations. It

is organised in three layers built upon the REST service architecture and supports a whole range of

resource hosting technologies such as HTTP, FTP, WebDav, JDBC compliant databases and cloud-

based file repositories.

Furthermore, this approach enables easy consideration of parameter variations including both

manual (user-driven) and stochastic input.




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Navigability; Common GUI to setup a project in simple steps and start working

The nD Navigator provides a common graphical user interface (GUI) for the entire process of defining

a project, assigning and sharing available data to perform a full range of simulations (e.g. passive, full

building, CFD), perform sensitivity analysis and visualize results.

Accessibility; Common GUI for easy access to several tools on the cloud from anywhere; use only

what you need, when you need it

Powerful and accurate tools are readily available for thermal energy analysis and simulation tools

through the same graphical use interface to facilitate sensitivity analysis of various parameters using

single-zone, multi-zone and full building energy/thermal solvers, and CFD simulations of indoor air.

Speedability; Maximize resources through automated processes and fast calculations with

parallel processing on the cloud

Computational services are ported and executed on a private cloud based on the OpenStack

framework. Moreover, the system is configured in a way that enables flexible transition to or use of

hybrid or public cloud infrastructures (tested with Amazon’s EC3).

Utilizability; Powerful pre- and post-processing tools to support the user for the preparation of

input data, interpretation of results and visualization of complex outputs

Easy to use pre-processing tools facilitate the preparation of all necessary input data to quickly setup

a project and start working. Everything is at the user’s fingertips and ready to access through a

common platform.

Post-processing is supported by powerful and flexible visualisation and decision support services

based on multi-eKPI analysis, to facilitate the user in handling, analysing and interpreting a multitude

of output data (results) by reducing their complexity.

From Complexity To Simplicity

Expandability; easy to integrate new tools and data in the IVEL

The well-structured, easy to understand and follow information and work flow facilitates future

enhancements and the integration of new data and resources, along with other simulation tools.




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Reliability; Common information basis grounded on established standards

The ISES platform is built entirely upon the use of established international, European or de facto

industry standards and not on specific legacy solutions or company specifications. This includes ICT

standards like REST, JASON, EXPRESS, XML and OWL, data representation standards in the AEC and

energy domains such as IFC (ISO 16739) and Omniclass (www.omniclass.org) as well as the

observance of EN ISO energy norms such as 50001:2011-12. All that provides a basis to rely on the

platform for a longer time period since norms and regulations are much more stable and broadly

recognised that short-term narrow-goal company solutions.

Improved energy-aware decision making; Guided, easy to understand and follow workflow and

powerful presentation and decision support tools enabling holistic knotworking procedures

Energy-aware decision making already in early design phases enabled via eeTemplates and a guided,

easy to understand and follow workflow realized via the ISES nD Navigator provide the means

enabling the efficient examination of numerous design alternatives on the basis of automatically

computed energy key performance indicators. The developed presentation and decision support

methods provide for taking informed decisions. Due to the realized web-based approach such

decisions can be taken collectively and holistically in knotworking web sessions involving all relevant

designers and stakeholders.

4.2 Weaknesses

HVAC Systems & Equipment

Heating, Ventilation and Air-Conditioning (HVAC) complete definitions are not handled currently in

great detail, for example, equipment types (e.g. AHUs, fan coil units, heat pumps, VRF, chilled

beams), air-side systems (e.g. CAV, VAV), direct expansion equipment and plant equipment (e.g.

different types of chillers, cooling towers, boilers, economizers, dehumidifiers, fans, pumps), and

system performance calculations. Spaces should retain all the space-zone-building information from

the energy‐enhanced BIM and combine additional information needed for the HVAC analysis - for

example, to describe the equipment type and efficiency, key components and controls etc., for

generating full, detailed definitions of air-side systems, chilled- or hot-water plants, as applicable.

The HVAC engineer could then investigate different equipment and system technical characteristics

with different performance.

Microclimate & Surrounding Buildings

The impact of microclimate and surrounding buildings on BEPS is currently not fully taken into

account and limited to the CFD simulations or the adaptation of input weather data to different

design conditions that resemble microclimate conditions around a building. Modelling the impact of

neighboring buildings may improve the estimations of the actual energy demand, especially for

buildings located in dense, urban environments. For example, adjacent buildings will reduce the

incident solar radiation on building facades and thus impact heating and cooling loads, daylight

availability etc. Similarly, accounting for urban heat island effects could further support strategic

decisions for sustainable urban planning.


http://www.omniclass.org/



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Coupling Hourly Thermal-CFD

The coupling of the thermal simulations and CFD analysis is currently handled by providing the initial

boundary conditions for selected extreme cases, which are then investigated through CFD

simulations. The best simulation strategy would be to reuse previous calculated results to generate

new results at the appropriate time step. This is a time consuming process in order to achieve

iteration convergence. Τo make it practical, one can consider the use of different scheduling

strategies to improve the CFD performance.

Material Embodied Energy

As buildings become more energy-efficient and more environmentally friendly building materials are

used for the construction of buildings, it becomes more important to also account for their embodied

energy and that of mechanical equipment and systems. The lower a material’s embodied energy, the

lower the amount of energy required for raw materials extraction, processing, manufacturing,

transportation and installation during construction, thus resulting to a lower overall environmental

impact. Actually, the embodied energy and carbon content of building materials can also serve as an

eKPI to reflect the sustainability of the materials used in a project. However, embodied energy data is

dependent on national data which is limited and usually available for building construction materials,

while very few include data on some particular mechanical systems and none comprehensively. The

energy‐enhanced BIM model could be used to generate actual material quantities that are then

linked to material libraries with embodied energy data or recycling potential after demolition (e.g.

use different fly-ash concentration in concrete, compare concrete or steel frame building

components, use raw or recycled aluminium). One could then identify the dominant materials that

have the highest combined contribution in terms of their quantity and embodied energy (or carbon)

impact as a result of their weight and density. This approach could strengthen future elaborations of

building performance to account for the total life-cycle energy demand that includes embodied,

operational and demolition (removal and recycling) energy.

Varying Utility Rates

Prices of energy carriers are currently considered at a flat rate. However, different energy price

scenarios may be applicable; for example, different electric rates on a seasonal basis, different day or

night rates and even demand charges. This can have great influence with regard to the proper choice

of a design solution.

Detailed Life-Cycle Cost Analysis

The long-term cost-effectiveness of a building project goes well beyond the initial design and first

cost for construction. Considering that a building’s life extends over several decades, the present

value of operational (energy) and maintenance costs may be as great as the initial construction cost

as a result of material or system deterioration. For example, a cost-effective initial design solution

may result to a low energy consumption (i.e. operational cost) but may have a high maintenance

cost. A Life-Cycle Cost Analysis (LCCA) process can evaluate a building’s economic performance over

its lifetime, balancing the initial (first cost) investment with the long-term cost of owning and

operating the building.




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Some of these weaknesses are already being addressed within a new project that was recently

initiated “Collaborative Holistic Design Laboratory and Methodology for Energy-Efficient Embedded

Buildings – eeEmbedded (2013-2017). The new effort (Figure 41) places an emphasis on simulations

and optimisations of HVAC and energy infrastructure systems that will be integrated in an enhanced

IVEL. Sensors for Building Automation and Control Systems (BACS) can be efficiently placed in the

building to collect relevant data that can then be optimized through simulations in order to optimize

the HVAC operation. Furthermore, CFD room simulations will support efforts to analyse and optimise

indoor thermal comfort conditions. Most important, LCCA will be handled in a more mature manner,

than the average values used in ISES. The model management & versioning will be extended as well,

by new knowledge based methods.

Figure 41: Overview of the envisaged new “eeEmbedded” project platform




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4.3 Opportunities

Exploitation Plan in Place

A solid exploitation plan (Protopsaltis et al. 2014b) was developed for the ISES project, classified into

products, services and knowledge. Interested third-parties can immediately engage and benefit from

using the IVEL, its services and tools.

The developed open source and freeware components will be used to develop a Virtual Energy Lab

Kernel to be maintained by a formal Operation Body or Association, with the responsibility for

coordinating its future development, versioning and licensing, including use of all licensed results.

Possible extensions aiming to provide improved functionality will be part of the freeware. The IVEL

Kernel Operation Body will provide support service to companies that wish to use or develop around

the IVEL, thus supporting the extensibility of the ISES exploitable knowledge and products by any

interested party in affordable manner. All parties that use the IVEL Kernel will be obliged to reference

and give credit to the ISES consortium and the European Commission, who own the Intellectual

Property Rights of the IVEL Kernel and the overall IVEL concept. A reference installation of the full

ISES platform and all other proprietary products of the project will be supported till the end of 2017.

Joint Exploitation eeB Platform

This new initiative was initiated by TUD-CIB, as the coordinator of ISES and several other EU projects,

in order to create an open source platform providing tools and specifications for an energy efficient

building (eeB) kernel for ICT interoperability. This non-profit, open and voluntary initiative intends to

bring together experts and ICT systems, tools, components and services from various European

projects that support the design, refurbishment, management and control of eeB.

Market Penetration

The eeB platform addresses a gap in the current design process which has been a barrier for SME

design and engineering firms, following the shift in energy and sustainable development driving the

market towards holistic energy analyses procedures for which solutions are not readily available in

the marketplace. Commercial vendors may initiate similar solutions with high penetration in their

customer base that can impact the direction for the eeB platform to adapt.

Standardization

Several standardization committees are active and can benefit from relevant ISES work and deliverables,

including: BuildingSMART standardisation activities regarding BIM/IFC (ISO/PAS 16739); BuildingSMART

standardisation activities regarding IDM (ISO 29481); ISO standardisation activities in conjunction with the

development of new products, namely part libraries (PLib), ISO 13584, and ISO “Building Construction” for

the organisation of information about construction works (ISO 12006, Parts 1-3).




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4.4 Threats

National Market, Codes & Standards

Use of BIM and other relevant standards or common market practices on national level may vary

considerably. Common information exchange and interoperability between different software

providers is based on current practices that may change as the industry evolves.

Future of the eeB Platform

Although organisational and maintenance efforts have been initiated, the attitude and support of

commercial vendors to the energy efficient building (eeB) platform is an open issue. Its success is

dependent on how the industry will move forward in support of the BIM processes in the energy

domain. Commercial vendors may also initiate solutions with high penetration in their customer base

that can change the direction for the eeB platform to adapt.

Maintenance

Upon completion of ISES there is a strong momentum and opportunities to continue maintenance and

further development of the final products within ongoing collaborations and voluntary agreements. It is

evident that future success will depend on the efforts to sustain and extend the current work.

IT-Performance & Security

BIM is supported and influenced by information systems and technology performance. A number of

new risks are inherent in the adoption of BIM, in particular the need to address cyber security in the

implementation of the collaborative processes and systems (Boyes 2014). The level of cyber security

protection depends on confidentiality – integrity – availability and some related legal issues, e.g.

protection of intellectual property and intellectual property rights, information management

responsibilities, including handling of cyber security incidents, and resolving BIM technology

disputes. Placing data on the cloud or on multi-access servers may be perceived as a threat since

they are vulnerable to hackers (Gunshon and Sherratt 2014). Even if there are strong arguments

against this, the fact remains that there is a strong perception amongst construction professionals

that the data contained within the BIM model itself could be unlawfully used against the building.

Practical BIM-related Issues

BIM continues to experience some practical problems related to complex spaces and space boundaries.

For example, it is a considerable effort to handle properly the complexity of building designs in practice

and move from the simplicity of a 1st level space boundary without inner boundaries to 2nd level with

inner boundaries between zones (in IFC: Type 2a) that are in fact not an exact match and thus mandate

a further even more cumbersome effort to define non-modelled items with 3rd level space boundaries

(in IFC: Type 2b) to close the gap. Similar issues arise for the definition of suspended ceilings, complex

wall constructions, protruding inner walls, round columns in walls generating a series of useless space

boundaries due to geometric approximation, as well as some geo-topological issues (e.g. accounting for

neighbouring buildings). All these require more complex processing.




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Shortage of Experienced End Users

BIM is becoming increasingly sophisticated and integrated. Architects, structural, mechanical

engineers, consultants and contractors may lack expertise for modelling in BIM or motivation for

implementing the BIM process. Such issues may occur as the highly specialized skills required are

currently relatively unique within the industry (Thurairajah and Goucher 2013). BIM implementation

mandates strong training requirements, which for some firms may be a challenge, due to the

investment costs and time involved. Client and industry demand will constitute the main driving

forces to alter the tendency for retaining the more familiar (traditional) ways of working.

Commercial Vendors

BIM is relatively new and its use in projects is still on low percentage level with regard to the overall

construction work done today even though in various countries such as Finland, Norway, the

Netherlands or the UK the situation is rapidly improving. However, there are already several BIM

providers that are penetrating the building industry fast. At the same time, open BIM communities are

being formed, e.g. GRAPHISOFT®, Tekla® and others are members of an alliance to promote open BIM

in the industry, which may capitalize on their commercial strength to infiltrate the market.




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5 Conclusions

The SWOT matrix (Figure 42) summarizes the main competitive advantages of the ISES process and

the IVEL.

Figure 42: ISES SWOT analysis matrix




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Conclusions from the pilots

The current IFC model schema is good enough as lifecycle repository for the energy domain;

Using dedicated model management services is necessary to close various information gaps

in the process;

There is a need for reusable BIM solutions for repeated project tasks, such as the transition

from architectural to energy-specific model views;

Using IFC for energy efficient building data modelling is an asset that should be further

expanded – and not negated – especially with regard to the overall lifecycle process and

costs.

How can you benefit?

Reduction of modelling effort by using building information models that are becoming state

of the art in designing buildings;

Easier access of complex energy analyses for architects and engineers (no need to be an

energy expert);

Virtual laboratory brings you closer to reality by combining different analyses tools and

enhanced climate, material & usage data;

Focus on the parameters of your interest by a sophisticated sensitivity analyses;

Results are made easy to understand by KPIs that bring it to the point for the designers and

clients.




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6 References

Balaras C., Dascalaki E., Guruz R., Katranuschkov, P. (2013): ISES D+ (Additional Deliverable): Energy-related key performance indicators within ISES, © ISES Consortium, Brussels.

Boyes H. (2014): Building Information Modelling (BIM): Addressing the Cyber Security Issues, Institution of Engineering and Technology, London, United Kingdom.

Christodoulou S., Xanthos S., Chari A., Georgiou C., Toxqui E. (2014): ISES Deliverable D12.2, Stochastic modelling approaches for holistic analysis of the energy performance of buildings, © ISES Consortium, Brussels.

Freudenberg P., Hoch R., Katranuschkov P., Baumgärtel K. (2014): ISES Deliverable D5.3: Prototype of the Simulation Configurator, © ISES Consortium, Brussels.

Gudnasson G., Balaras C., Mansperger T., U. Leskovšek, P. Katranuschkov (2014): ISES Deliverable D4.4: Characteristic Energy Profile and Consumption Patterns for the ISES Virtual Energy Lab, © ISES Consortium, Brussels.

Gunshon K., Sherratt F. (2014): Is BIM a security threat to the built environment?, Chartered Institute of Building, UK.

ISO 16739:2013, Industry Foundation Classes (IFC) for data sharing in the construction and facility management industries, International Organization for Standardization. Geneva.

ISO 29481-1:2010, Building information modelling -- Information delivery manual -- Part 1: Methodology and format, International Organization for Standardization. Geneva.

Jung R., Mansperger T., Guruz R., Forns-Samso F., Gudnason G., Kaiser J., Katranuschkov P., Kavcic M., Laine T., Zahedi A., Zingel U., Zschippang S. (2012): ISES Deliverable D1.2: Use Case Scenarios and Requirements Specification, © ISES Consortium, Brussels.

Kadolsky M., Katranuschkov P., Dolenc M., Baumgärtel K., Klinc R. & Gudnason G. (2014): ISES Deliverable D3.1: Ontology specification, © ISES Consortium, Brussels.

Laine T., Forns-Samso F., Idman T., Gürtler M. (2014a): ISES Deliverable D6.3: Prototype of the simulation evaluation service and the multi-model navigator, © ISES Consortium, Brussels.

Laine T., Forns-Samso F., Katranuschkov P., Hoch R., Freudenberg P. (2014b): Application of multi-step simulation and multi-eKPI sensitivity analysis in building energy design optimization, ECPPM 2014 European Conference on Product and Process Modelling, Vienna, Austria.

Lombriser L., Abplanalp P. A. (2010): Strategisches Management (5th Ed.), Versus Verlag AG, Zürich, ISBN 978-3-03909-149-2.

Protopsaltis B., Rekouniotis T., Pappou T., Balaras C., Laine T., Hoch R., Noack F., Katranuschkov P. (2014a): ISES Deliverable D9.1.1: First public demonstrator of the Virtual Energy Lab and developed ee-performance indicators, © ISES Consortium, Brussels.

Protopsaltis B., Hadjicostas T., Scherer R., Katranuschkov P., Grunewald J., Laine T., Mansperger T., Gudnason G., Dolenc M., Balaras C., Kavcic M. (2014b): ISES Deliverable D8.4.3: Final Exploitation Plan, © ISES Consortium, Brussels.

Protopsaltis B., Rekouniotis T., Katranuschkov P. (2014c): ISES Deliverable D5.2: Prototype of the Multi-Model Combiner, © ISES Consortium, Brussels.

Thurairajah N., Goucher D. (2013): Advantages and Challenges of Using BIM: a Cost Consultant’s Perspective, 49th ASC Annual International Conference Proceedings, San Luis Obispo, CA.


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