the future of building modeling modeling operations and ... buildings trac… · modeling...

The future of building modeling

Modeling operations and control strategies with Modelica and the Building Controls Virtual Test Bed

Michael Wetter

Simulation Research Group

August 12, 2012

1

Higher flexibility is needed from simulation tools to support new building technologies and building operations

2

Rapid prototyping

Real-time monitoring to verifydesign intent

Development of control sequences

Workforce training

REAL-TIME BUILDING ENERGY SIMULATION USING ENERGYPLUS AND THE BUILDING CONTROLS VIRTUAL TEST BED

Xiufeng Pang1, Prajesh Bhattacharya1, Zheng O’Neill2, Philip Haves1, Michael Wetter1, and

Trevor Bailey2 1 Lawrence Berkeley National Laboratory, Berkeley, CA, USA

2 United Technologies Research Center, East Hartford, CT, USA

ABSTRACT Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures (Haves et al., 2001, Lee et al., 2007). To address this problem, the paper presents a simulation-based whole building performance monitoring tool that allows a comparison of building actual performance and expected performance in real time. The tool continuously acquires relevant building model input variables from existing Energy Management and Control System (EMCS). It then reports expected energy consumption as simulated of EnergyPlus. The Building Control Virtual Test Bed (BCVTB) is used as the software platform to provide data linkage between the EMCS, an EnergyPlus model, and a database. This paper describes the integrated real-time simulation environment. A proof-of-concept demonstration is also presented in the paper.

INTRODUCTION EnergyPlus (US DOE, 2010; Crawley et al., 2001) is a detailed first principles based simulation tool that calculates the building heating and cooling loads, and disaggregates energy end uses and other variables required for a comprehensive comparison of simulated and measured performance . Conventionally, EnergyPlus is used for off-line building energy simulation analyze design for new construction and retrofit, size HVAC equipment, and model energy and water use in buildings. With the increasing need to improve building performance, the use of simulation to assess the actual performance of buildings is starting to gain more attention (Haves et al. 2001, Liu et al. 2003, Ramirez et al. 2005). This paper describes a proof-of-concept implementation of EnergyPlus in a real-time application, which represents a step towards the development and deployment of simulation-based building performance assessment techniques. Real-time building simulation, as opposed to off-line building simulation, refers to the use of a building model whose simulation time is synchronized with real time, as represented by the computer clock. Updated values of the input variables are acquired

dynamically at each step-time. With the wide deployment of Energy Management and Control Systems (EMCS) in buildings and the development of open protocols such as BACnet, the sensor and control signal information from various component and systems in a building is more acccessible (Salsbury et al. 2000). This makes it possible to acquire the real-time EnergyPlus dynamic input variables from the EMCS including but not limited to weather data, operation schedules, control set points. However, the EMCS does not normally have all the necessary model input variables that are needed for real-time simulation, e.g. solar radiation, wind speed and direction and additional instrumentation is required to accomadate these needs. The Building Controls Virtual Test Bed (BCVTB), recently developed by Lawrence Berkeley National Laboratory (LBNL), has provided a platform to synchronize EnergyPlus simulation time to real-time and exchange data with EMCS in the real-time mode as well (Wetter, 2010; Nouidui et al., 2011). It is an extension of Ptolemy II, a software environment for heterogeneous modeling and simulation. Ptolemy II is a free open-source software developed at the University of California, Berkeley. This paper describes the integrated real-time simulation environment as well as the additional instrumentation required by the real-time simulation. A proof-of-concept demonstration is then described.

SYSTEM INFRASTRUCTURE Figure 1 shows the overall system architecture. It consists of two sub-systems: (i) the EMCS that serves as the data acquisition system and (ii) the real-time simulation environment that integrates the EnergyPlus simulation, database and the data

Figure 1 Overall system architecture

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Energy code development

Modular tools provide flexibility needed to support model use along product and building life cycle and leverage investments of multi-disciplinary teamsFlexible modeling using open-source Modelica language

Supports • rapid addition of new component

and system models,• modeling actual feedback control

(continuous time, discrete time, event-driven, state machines),

• emulation of non-ideal and/or faulty equipment and controls,

• initialization of states (history) to test different control sequences in real-time

• use of advanced solvers as needed for controls simulation

Linking tools with measurements using the Building Controls Virtual Test Bed and the Functional Mockup Interface

Supports• integrated analysis through run-

time coupling of simulators• model-based feedback control,

monitoring and hardware-in-the-loop verification through coupling of models with actual equipment

• exchange of models and simulation programs in a standardized format

3

Rapid prototyping allowed comparison of energy efficiency and comfort improvement of heating system with decentralized pumps and controls

4

Wilo Geniax radiator pump

Within one week, developed boiler model, radiator model, simple room model and two system models, consisting of 2400 component models.M. Wetter, Building Simulation, 2009

Figure 4: View of the two-room model in the graph-ical model editor of Dymola.

Fig. 4 is a view of a subset of the system modelas displayed by the graphical model editor of Dy-mola. Each icon encapsulates a model, which mayencapsulate additional models to enable a hierarchi-cal model definition. In Fig. 4, on the left are in-put signals for the room temperature setpoint and theoutside air. Next, there are vertical lines to connectfluid ports at the bottom and top of the floor. (For thetop floor of the building, the model translator will setthe mass flow rates in these pipe segments to zero,as the top ports are not connected.) In the left pipe,we placed a model that computes flow friction. Thegrey boxes in the fluid lines are finite volume mod-els for the radiators. To the right of the radiators arethe circulation pumps, and on top of the radiators arethe room models. The room models contain finitevolume models for computing the transient heat flowthrough the building constructions. Input to the roommodels are the outside temperatures and heat gains.The heat gains were defined by a time table for theleft room, but they were set to zero for the right room.The red connection lines connect the room modelsto the radiators. They equate the temperatures andbalance the convective and radiative heat flows, re-spectively, between radiator and rooms. There is alsoa heat flow connection between the rooms for inter-zonal heat transfer. Above the room models are thepump controllers. This two-room model is then in-stantiated nine times to form a three-storey housewith three vertical distribution lines, and the distri-bution lines are connected to a plant model that con-tains the boiler and the centralized system controller.The total system model is composed of 2400 compo-nent models that form a differential algebraic equa-tion system with 13,200 equations. After the sym-bolic manipulations, there were 8700 equations with300 state variables. Building the system models forthe TRV and the DP systems, including the models

0 2 4 6 8 10 12 14 16 18 20 22 24204060

T [°C

]

Boiler set point, supply and return temperatures

0 2 4 6 8 10 12 14 16 18 20 22 241820

T [°C

]

Room temperatures

0 2 4 6 8 10 12 14 16 18 20 22 240

0.51

Boiler and radiator valve signals

y

0 2 4 6 8 10 12 14 16 18 20 22 24012

Normalized radiator mass flow rates

m /

m0

(a) TRV system

0 2 4 6 8 10 12 14 16 18 20 22 24204060

T [°C

]


0 2 4 6 8 10 12 14 16 18 20 22 241820

T [°C

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Room temperatures

0 2 4 6 8 10 12 14 16 18 20 22 240

0.51

Boiler and radiator pump signals

y

0 2 4 6 8 10 12 14 16 18 20 22 24012


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Figure 5: Comparison of the dynamic system re-sponse of the TRV and DP systems for a lightweightbuilding. The lower three subfigures show the trajec-tories of the four rooms that are closest and farthestaway from the boiler, with the solid lines correspond-ing to the rooms with heat gains.

for the room, the radiator, the boiler and a first ver-sion of the controllers, took about a week of labor.

Fig. 5 shows the trajectories computed by the twosystem models. In the TRV system, the radiatorvalves open at night since the room temperature fallsbelow their set point temperatures of 20!C. Thiscauses the radiators to release heat to the room, al-though at a lower rate because of the lower supplywater temperature. However, in the DP system, theradiator valves and the boiler switch off while theroom temperature is above the night setback temper-ature, which causes a larger reduction in room tem-perature at night.

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Figure 4: View of the two-room model in the graph-ical model editor of Dymola.

Fig. 4 is a view of a subset of the system modelas displayed by the graphical model editor of Dy-mola. Each icon encapsulates a model, which mayencapsulate additional models to enable a hierarchi-cal model definition. In Fig. 4, on the left are in-put signals for the room temperature setpoint and theoutside air. Next, there are vertical lines to connectfluid ports at the bottom and top of the floor. (For thetop floor of the building, the model translator will setthe mass flow rates in these pipe segments to zero,as the top ports are not connected.) In the left pipe,we placed a model that computes flow friction. Thegrey boxes in the fluid lines are finite volume mod-els for the radiators. To the right of the radiators arethe circulation pumps, and on top of the radiators arethe room models. The room models contain finitevolume models for computing the transient heat flowthrough the building constructions. Input to the roommodels are the outside temperatures and heat gains.The heat gains were defined by a time table for theleft room, but they were set to zero for the right room.The red connection lines connect the room modelsto the radiators. They equate the temperatures andbalance the convective and radiative heat flows, re-spectively, between radiator and rooms. There is alsoa heat flow connection between the rooms for inter-zonal heat transfer. Above the room models are thepump controllers. This two-room model is then in-stantiated nine times to form a three-storey housewith three vertical distribution lines, and the distri-bution lines are connected to a plant model that con-tains the boiler and the centralized system controller.The total system model is composed of 2400 compo-nent models that form a differential algebraic equa-tion system with 13,200 equations. After the sym-bolic manipulations, there were 8700 equations with300 state variables. Building the system models forthe TRV and the DP systems, including the models

0 2 4 6 8 10 12 14 16 18 20 22 24204060

T [°C

]


0 2 4 6 8 10 12 14 16 18 20 22 241820

T [°C

]

Room temperatures

0 2 4 6 8 10 12 14 16 18 20 22 240

0.51

Boiler and radiator valve signals

y

0 2 4 6 8 10 12 14 16 18 20 22 24012


m /

m0

(a) TRV system

0 2 4 6 8 10 12 14 16 18 20 22 24204060

T [°C

]


0 2 4 6 8 10 12 14 16 18 20 22 241820

T [°C

]

Room temperatures

0 2 4 6 8 10 12 14 16 18 20 22 240

0.51

Boiler and radiator pump signals

y

0 2 4 6 8 10 12 14 16 18 20 22 24012


m /

m0

(b) DP system

Figure 5: Comparison of the dynamic system re-sponse of the TRV and DP systems for a lightweightbuilding. The lower three subfigures show the trajec-tories of the four rooms that are closest and farthestaway from the boiler, with the solid lines correspond-ing to the rooms with heat gains.

for the room, the radiator, the boiler and a first ver-sion of the controllers, took about a week of labor.

Fig. 5 shows the trajectories computed by the twosystem models. In the TRV system, the radiatorvalves open at night since the room temperature fallsbelow their set point temperatures of 20!C. Thiscauses the radiators to release heat to the room, al-though at a lower rate because of the lower supplywater temperature. However, in the DP system, theradiator valves and the boiler switch off while theroom temperature is above the night setback temper-ature, which causes a larger reduction in room tem-perature at night.

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Conventional thermostatic radiator valves Radiator pumps

www.geniax.de

System schematics of radiator valve system

Modelica model of building, HVAC and control sequence

Real-time monitoring informs operator about energy waste based on comparison of simulated and measured building

5

Figure 6 Building total electric power comparison between real-time simulation and off-line simulation using the customized weather file

Figure 7 Building total electric power comparison between real-time simulation and actual measurement

Figure 8 Cooling electric power comparison between real-time simulation and actual measurement

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Figure 6 Building total electric power comparison between real-time simulation and off-line simulation using the customized weather file

Figure 7 Building total electric power comparison between real-time simulation and actual measurement

Figure 8 Cooling electric power comparison between real-time simulation and actual measurement

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REAL-TIME BUILDING ENERGY SIMULATION USING ENERGYPLUS AND THE BUILDING CONTROLS VIRTUAL TEST BED

Xiufeng Pang1, Prajesh Bhattacharya1, Zheng O’Neill2, Philip Haves1, Michael Wetter1, and

Trevor Bailey2 1 Lawrence Berkeley National Laboratory, Berkeley, CA, USA

2 United Technologies Research Center, East Hartford, CT, USA

ABSTRACT Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures (Haves et al., 2001, Lee et al., 2007). To address this problem, the paper presents a simulation-based whole building performance monitoring tool that allows a comparison of building actual performance and expected performance in real time. The tool continuously acquires relevant building model input variables from existing Energy Management and Control System (EMCS). It then reports expected energy consumption as simulated of EnergyPlus. The Building Control Virtual Test Bed (BCVTB) is used as the software platform to provide data linkage between the EMCS, an EnergyPlus model, and a database. This paper describes the integrated real-time simulation environment. A proof-of-concept demonstration is also presented in the paper.

INTRODUCTION EnergyPlus (US DOE, 2010; Crawley et al., 2001) is a detailed first principles based simulation tool that calculates the building heating and cooling loads, and disaggregates energy end uses and other variables required for a comprehensive comparison of simulated and measured performance . Conventionally, EnergyPlus is used for off-line building energy simulation analyze design for new construction and retrofit, size HVAC equipment, and model energy and water use in buildings. With the increasing need to improve building performance, the use of simulation to assess the actual performance of buildings is starting to gain more attention (Haves et al. 2001, Liu et al. 2003, Ramirez et al. 2005). This paper describes a proof-of-concept implementation of EnergyPlus in a real-time application, which represents a step towards the development and deployment of simulation-based building performance assessment techniques. Real-time building simulation, as opposed to off-line building simulation, refers to the use of a building model whose simulation time is synchronized with real time, as represented by the computer clock. Updated values of the input variables are acquired

dynamically at each step-time. With the wide deployment of Energy Management and Control Systems (EMCS) in buildings and the development of open protocols such as BACnet, the sensor and control signal information from various component and systems in a building is more acccessible (Salsbury et al. 2000). This makes it possible to acquire the real-time EnergyPlus dynamic input variables from the EMCS including but not limited to weather data, operation schedules, control set points. However, the EMCS does not normally have all the necessary model input variables that are needed for real-time simulation, e.g. solar radiation, wind speed and direction and additional instrumentation is required to accomadate these needs. The Building Controls Virtual Test Bed (BCVTB), recently developed by Lawrence Berkeley National Laboratory (LBNL), has provided a platform to synchronize EnergyPlus simulation time to real-time and exchange data with EMCS in the real-time mode as well (Wetter, 2010; Nouidui et al., 2011). It is an extension of Ptolemy II, a software environment for heterogeneous modeling and simulation. Ptolemy II is a free open-source software developed at the University of California, Berkeley. This paper describes the integrated real-time simulation environment as well as the additional instrumentation required by the real-time simulation. A proof-of-concept demonstration is then described.

SYSTEM INFRASTRUCTURE Figure 1 shows the overall system architecture. It consists of two sub-systems: (i) the EMCS that serves as the data acquisition system and (ii) the real-time simulation environment that integrates the EnergyPlus simulation, database and the data

Figure 1 Overall system architecture

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Application: Performance monitoring and assessment of a real building using the BACnetReader !   Chicago, IL !   Two-storey building (70,000 ft2)

X. Pang et al., Building Simulation, 2011

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To find in real-time closed-loop control the optimal control sequence, Modelica was used to compute energy use of different control sequences using a dynamic model.The optimal control signal was sent to the Building Controls Virtual Test Bed, which sent the control action to the physical test cell, and obtained from the test cell new temperatures and radiation data for the next optimization.

Dynamic Modelica model of test cell

Emulation of faulty operations allows workforce training in fault detection and diagnostics

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Teach correct and faulty operation of HVAC systems.

Ongoing: Expansion to whole-building level, including lighting and daylighting.

http://www.learnHVAC.org http://www.learngreenbuildings.org/

Instructors at community colleges configure fault-detection scenarios to train building professionals.

3D web-based interface with physics-based Modelica models to emulate system response, including control feedbacks and equipment faults.

http://www.learngreenbuildings.org

http://www.learnHVAC.org

http://www.learnHVAC.org

http://www.learngreenbuildings.org

Dynamic models assess system-level energy & water efficiency of residential water heating in support of energy code development

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Generation Currently 50-75% efficient.

- Storage tank- Tankless- Tankless & buffer tank- Solar thermal

Distribution Currently 40-75% efficient.

- Topology of pipe network- Pipe diameter and insulation- Distributed buffer tanks

Draw patternsImpact of user behavior

GoalsSupport revision of Title 24.Design recommendations.Input for behavioral programs.

Multidisciplinary collaborative projects for next-generation computational tools for engineered systems

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ModelicaRapid prototyping & modeling of actual feedback control coupled to dynamic plant models

Three ITEA2 projects: 370 person years, 54 million !

Building Controls Virtual Test BedRun-time coupling of simulators,control systems and data acquisition systems

Functional Mockup InterfaceStandard for coupling of simulators and exchange of models.

ITEA project: 30 partners, > 175 person years, > 28 million !, July 2008 - June 2011

What’s nextInternational Energy Agency Annex 60

“New generation computational tools for building and community energy systems based on the Modelica and Functional Mockup Interface standards.”

5 years, 11 countries, led by LBNL and RWTH Aachen

the future of building modeling modeling operations and ... buildings trac… · modeling...

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