Building performance simulation: Difference between revisions

Revision as of 14:11, 13 April 2020

Revision as of 00:19, 27 May 2020

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== Introduction ==

From a physical point of view, a building is a very complex system, influenced by a wide range of parameters. A [[Simulation modeling|simulation model]] is an abstraction of the real building which allows to consider the influences on high level of detail and to analyze key performance indicators without cost-intensive measurements. BPS is a technology of considerable potential that provides the ability to quantify and compare the relative cost and performance attributes of a proposed design in a realistic manner and at relatively low effort and cost. Energy demand, indoor environmental quality (incl. [[Thermal comfort|thermal]] and visual comfort, [[indoor air quality]] and moisture phenomena), [[HVAC]] and renewable system performance, urban level modeling, [[building automation]], and operational optimization are important aspects of BPS.<ref name=":0">{{Cite book|title=Energy simulation in building design|last=Clarke|first=J. A.|date=2001|publisher=Butterworth-Heinemann|isbn=978-0750650823|edition=2nd|___location=Oxford|pages=|oclc=46693334}}</ref><ref name=":6">{{Cite book|title=Building performance simulation for design and operation|date=2011|publisher=Spon Press|others=Hensen, Jan., Lamberts, Roberto.|isbn=9780415474146|___location=Abingdon, Oxon|oclc=244063540}}</ref><ref name=":32">{{Cite journal|lastlast1=Clarke|firstfirst1=J. A.|last2=Hensen|first2=J. L. M.|date=2015-09-01|title=Integrated building performance simulation: Progress, prospects and requirements|journal=Building and Environment|series=Fifty Year Anniversary for Building and Environment|volume=91|pages=294–306|doi=10.1016/j.buildenv.2015.04.002|url=https://strathprints.strath.ac.uk/52580/1/Clarke_Hensen_BuildEnv_2015_Integrated_building_performance_simulation_progress_prospects_and_requirements.pdf}}</ref>

Over the last six decades, numerous BPS computer programs have been developed. The most comprehensive listing of BPS software can be found in the BEST directory.<ref>{{Cite web|url=http://www.buildingenergysoftwaretools.com/|title=Best Directory {{!}} Building Energy Software Tools|website=www.buildingenergysoftwaretools.com|language=en|access-date=2017-11-07}}</ref> Some of them only cover certain parts of BPS (e.g. climate analysis, thermal comfort, energy calculations, plant modeling, daylight simulation etc.). The core tools in the field of BPS are multi-___domain, dynamic, whole-building simulation tools, which provide users with key indicators such as heating and cooling load, energy demand, temperature trends, humidity, thermal and visual comfort indicators, air pollutants, ecological impact and costs.<ref name=":32" /><ref name=":4">{{Cite journal|lastlast1=Crawley|firstfirst1=Drury B.|last2=Hand|first2=Jon W.|last3=Kummert|first3=Michaël|last4=Griffith|first4=Brent T.|date=2008-04-01|title=Contrasting the capabilities of building energy performance simulation programs|journal=Building and Environment|series=Part Special: Building Performance Simulation|volume=43|issue=4|pages=661–673|doi=10.1016/j.buildenv.2006.10.027|url=https://strathprints.strath.ac.uk/6555/6/strathprints006555.pdf}}</ref>

A typical building simulation model has inputs for local weather; building geometry; [[building envelope]] characteristics; internal heat gains from [[lighting]], occupants and [[Plug load|equipment loads]]; heating, ventilation, and cooling (HVAC) system specifications; operation schedules and control strategies.<ref name=":0" /> The ease of input and accessibility of output data varies widely between BPS tools. Advanced whole-building simulation tools are able to consider almost all of the following in some way with different approaches.

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The history of BPS is approximately as long as that of [[computer]]s. The very early developments in this direction started in the late 1950s and early 1960s in the United States and Sweden. During this period, several methods had been introduced for analyzing single system components (e.g. gas boiler) using steady state calculations. The very first reported simulation tool for buildings was '''BRIS''', introduced in 1963 by the [[Royal Institute of Technology]] in Stockholm.<ref>{{cite journal|last1=Brown|first1=Gösta|title=The BRIS simulation program for thermal design of buildings and their services|journal=Energy and Buildings|date=January 1990|volume=14|issue=4|pages=385–400|doi=10.1016/0378-7788(90)90100-W}}</ref> Until the late 1960s, several models with hourly resolution had been developed focusing on energy assessments and heating/cooling load calculations. This effort resulted in more powerful simulation engines released in the early 1970s, among those were BLAST, DOE-2, [[ESP-r]], HVACSIM+ and [[TRNSYS]].<ref>{{Cite web|url=http://www.ibpsa.org/%5Cproceedings%5CBS1999%5CBS99_P-01.pdf|title=Early history and future prospects of building system simulation|last=Kusuda|first=T.|date=1999|website=IBPSA Proceedings|access-date=2017-07-07}}</ref> In the United States, the [[1970s energy crisis]] intensified these efforts, as reducing the energy consumption of buildings became an urgent domestic policy interest. The energy crisis also initiated development of U.S. building energy standards, beginning with [[ASHRAE 90.1#Standard 90-1975|ASHRAE 90-75]].<ref>{{Cite journal|last=Sukjoon|first=Oh|date=2013-08-19|title=Origins of Analysis Methods in Energy Simulation Programs Used for High Performance Commercial Buildings|url=http://oaktrust.library.tamu.edu/handle/1969.1/151151|language=en}}</ref>

The development of building simulation represents a combined effort between academia, governmental institutions, industry, and professional organizations. Over the past decades the building simulation discipline has matured into a field that offers unique expertise, methods and tools for [[building performance]] evaluation. Several review papers and state of the art analysis were carried out during that time giving an overview about the development.<ref>{{Cite journal|lastlast1=Augenbroe|firstfirst1=Godfried|last2=Hensen|first2=Jan|date=2004-08-01|title=Simulation for better building design|journal=Building and Environment|series=Building Simulation for Better Building Design|volume=39|issue=8|pages=875–877|doi=10.1016/j.buildenv.2004.04.001}}</ref><ref>Hensen, J. (2006). [http://www.janhensen.nl/publications_folder/06_ibpsa-cz_hensen.pdf About the current state of building performance simulation and ibpsa]. In ''4th national IBPS-CZ conference'' (p. 2).</ref><ref>{{Cite journal|lastlast1=Wang|firstfirst1=Haidong|last2=Zhai|first2=Zhiqiang (John)|date=2016-09-15|title=Advances in building simulation and computational techniques: A review between 1987 and 2014|journal=Energy and Buildings|volume=128|pages=319–335|doi=10.1016/j.enbuild.2016.06.080}}</ref>

In the 1980s, a discussion about future directions for BPS among a group of leading building simulation specialists started. There was a consensus that most of the tools, that had been developed until then, were too rigid in their structure to be able to accommodate the improvements and flexibility that would be called for in the future.<ref>Clarke, J.A.; Sowell, E.F.; the Simulation Research Group (1985): ''A Proposal to Develop a Kernel System for the Next Generation of Building Energy Simulation Software'', Lawrence Berkeley Laboratory, Berkeley, CA, Nov. 4, 1985</ref> Around this time, the very first equation-based building simulation environment '''ENET'''<ref>Low, D. and Sowell, E.F. (1982): ''ENET, a PC-based building energy simulation system,'' Energy Programs Conference, IBM Real Estate and Construction Division, Austin, Texas (1982), pp. 2-7</ref> was developed, which provided the foundation of '''SPARK'''. In 1989, Sahlin and Sowell presented a '''[[Neutral Model Format]]''' (NMF) for building simulation models, which is used today in the commercial software [[IDA Indoor Climate and Energy|IDA ICE]].<ref>Sahlin, P. and Sowell, E.F. (1989). A neutral format for building simulation models, Proceedings of the Second International IBPSA Conference, Vancouver, BC, Canada, pp. 147-154, http://www.ibpsa.org/proceedings/BS1989/BS89_147_154.pdf</ref> Four years later, Klein introduced the '''[[Engineering Equation Solver]]''' (EES)<ref>{{Cite journal|last=Klein|first=S. A.|date=1993-01-01|title=Development and integration of an equation-solving program for engineering thermodynamics courses|journal=Computer Applications in Engineering Education|language=en|volume=1|issue=3|pages=265–275|doi=10.1002/cae.6180010310|issn=1099-0542}}</ref> and in 1997, Mattsson and Elmqvist reported on an international effort to design '''[[Modelica]]'''.<ref>{{Cite journal|lastlast1=Mattsson|firstfirst1=Sven Erik|last2=Elmqvist|first2=Hilding|date=April 1997|title=Modelica - An International Effort to Design the Next Generation Modeling Language|journal=IFAC Proceedings Volumes|series=7th IFAC Symposium on Computer Aided Control Systems Design (CACSD '97), Gent, Belgium, 28–30 April|volume=30|issue=4|pages=151–155|doi=10.1016/S1474-6670(17)43628-7|citeseerx=10.1.1.16.5750}}</ref>

BPS still presents challenges relating to problem representation, support for performance appraisal, enabling operational application, and delivering user education, training, and accreditation. Clarke (2015) describes a future vision of BPS with the following, most important tasks which should be addressed by the global BPS community.<ref>{{Cite journal|last=Clarke|first=Joe|date=2015-03-04|title=A vision for building performance simulation: a position paper prepared on behalf of the IBPSA Board|journal=Journal of Building Performance Simulation|volume=8|issue=2|pages=39–43|doi=10.1080/19401493.2015.1007699|issn=1940-1493|doi-access=free}}</ref>

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== Accuracy ==

In the context of building simulation models, '''error''' refers to the discrepancy between simulation results and the actual measured performance of the building. There are normally occurring [[uncertainties in building design and building energy assessment|uncertainties in building design and building assessment]], which generally stem from approximations in model inputs, such as occupancy behavior. '''Calibration''' refers to the process of "tuning" or adjusting assumed simulation model inputs to match observed data from the utilities or [[Building Management System]] (BMS).<ref>{{Cite journal|lastlast1=Raftery|firstfirst1=Paul|last2=Keane|first2=Marcus|last3=Costa|first3=Andrea|date=2011-12-01|title=Calibrating whole building energy models: Detailed case study using hourly measured data|journal=Energy and Buildings|volume=43|issue=12|pages=3666–3679|doi=10.1016/j.enbuild.2011.09.039}}</ref><ref>{{Cite journal|last=Reddy|first=T. Agami|date=2006|title=Literature Review on Calibration of Building Energy Simulation Programs: Uses, Problems, Procedures, Uncertainty, and Tools.|url=http://web.a.ebscohost.com/abstract?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=00012505&AN=21489891&h=p8ojDgTz25mLEtPl4J%2f86zfAUGKoYzTVsDcvoE2LFrNnW0vox%2bp0QW8edSwoCq%2bDwUzsmlj6wPJVrbTSmFK79g%3d%3d&crl=c&resultNs=AdminWebAuth&resultLocal=ErrCrlNotAuth&crlhashurl=login.aspx%3fdirect%3dtrue%26profile%3dehost%26scope%3dsite%26authtype%3dcrawler%26jrnl%3d00012505%26AN%3d21489891|journal=ASHRAE Transactions|volume=112 |issue=1|pages=226–240|via=}}</ref><ref>{{Cite journal|lastlast1=Heo|firstfirst1=Y.|last2=Choudhary|first2=R.|last3=Augenbroe|first3=G.A.|title=Calibration of building energy models for retrofit analysis under uncertainty|journal=Energy and Buildings|language=en|volume=47|pages=550–560|doi=10.1016/j.enbuild.2011.12.029|year=2012}}</ref>

The number of publications dealing with accuracy in building modeling and simulation increased significantly over the past decade. Many papers report large gaps between simulation results and measurements,<ref>{{Cite journal|lastlast1=Coakley|firstfirst1=Daniel|last2=Raftery|first2=Paul|last3=Keane|first3=Marcus|date=2014-09-01|title=A review of methods to match building energy simulation models to measured data|journal=Renewable and Sustainable Energy Reviews|volume=37|pages=123–141|doi=10.1016/j.rser.2014.05.007|url=https://escholarship.org/uc/item/88z3g017}}</ref><ref>{{Cite journal|lastlast1=Li|firstfirst1=Nan|last2=Yang|first2=Zheng|last3=Becerik-Gerber|first3=Burcin|last4=Tang|first4=Chao|last5=Chen|first5=Nanlin|title=Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures?|journal=Applied Energy|volume=159|pages=196–205|doi=10.1016/j.apenergy.2015.09.001|year=2015|doi-access=free}}</ref><ref>{{Cite journal|lastlast1=Hong|firstfirst1=Taehoon|last2=Kim|first2=Jimin|last3=Jeong|first3=Jaemin|last4=Lee|first4=Myeonghwi|last5=Ji|first5=Changyoon|title=Automatic calibration model of a building energy simulation using optimization algorithm|journal=Energy Procedia|volume=105|pages=3698–3704|doi=10.1016/j.egypro.2017.03.855|year=2017|doi-access=free}}</ref><ref>{{Cite journal|lastlast1=Mustafaraj|firstfirst1=Giorgio|last2=Marini|first2=Dashamir|last3=Costa|first3=Andrea|last4=Keane|first4=Marcus|title=Model calibration for building energy efficiency simulation|journal=Applied Energy|language=en|volume=130|pages=72–85|doi=10.1016/j.apenergy.2014.05.019|year=2014}}</ref> while other studies show that they can match very well.<ref>{{Cite journal|lastlast1=Christensen|firstfirst1=Jørgen Erik|last2=Chasapis|first2=Kleanthis|last3=Gazovic|first3=Libor|last4=Kolarik|first4=Jakub|date=2015-11-01|title=Indoor Environment and Energy Consumption Optimization Using Field Measurements and Building Energy Simulation|journal=Energy Procedia|series=6th International Building Physics Conference, IBPC 2015|volume=78|pages=2118–2123|doi=10.1016/j.egypro.2015.11.281|doi-access=free}}</ref><ref>{{Cite journal|lastlast1=Cornaro|firstfirst1=Cristina|last2=Puggioni|first2=Valerio Adoo|last3=Strollo|first3=Rodolfo Maria|date=2016-06-01|title=Dynamic simulation and on-site measurements for energy retrofit of complex historic buildings: Villa Mondragone case study|journal=Journal of Building Engineering|volume=6|pages=17–28|doi=10.1016/j.jobe.2016.02.001}}</ref><ref>{{Cite journal|lastlast1=Cornaro|firstfirst1=Cristina|last2=Rossi|first2=Stefania|last3=Cordiner|first3=Stefano|last4=Mulone|first4=Vincenzo|last5=Ramazzotti|first5=Luigi|last6=Rinaldi|first6=Zila|title=Energy performance analysis of STILE house at the Solar Decathlon 2015: lessons learned|journal=Journal of Building Engineering|volume=13|pages=11–27|doi=10.1016/j.jobe.2017.06.015|year=2017}}</ref> The reliability of results from BPS depends on many different things, e.g. on the quality of input data,<ref>{{Cite journal|lastlast1=Dodoo|firstfirst1=Ambrose|last2=Tettey|first2=Uniben Yao Ayikoe|last3=Gustavsson|first3=Leif|title=Influence of simulation assumptions and input parameters on energy balance calculations of residential buildings|journal=Energy|volume=120|pages=718–730|doi=10.1016/j.energy.2016.11.124|year=2017}}</ref> the competence of the simulation engineers<ref>{{Cite journal|lastlast1=Imam|firstfirst1=Salah|last2=Coley|first2=David A|last3=Walker|first3=Ian|date=2017-01-18|title=The building performance gap: Are modellers literate?|journal=Building Services Engineering Research and Technology|language=en|volume=38|issue=3|pages=351–375|doi=10.1177/0143624416684641|url=http://opus.bath.ac.uk/53934/1/ImamColeyWalker2017.pdf}}</ref> and on the applied methods in the simulation engine.<ref>{{Cite journal|lastlast1=Nageler|firstfirst1=P.|last2=Schweiger|first2=G.|last3=Pichler|first3=M.|last4=Brandl|first4=D.|last5=Mach|first5=T.|last6=Heimrath|first6=R.|last7=Schranzhofer|first7=H.|last8=Hochenauer|first8=C.|title=Validation of dynamic building energy simulation tools based on a real test-box with thermally activated building systems (TABS)|journal=Energy and Buildings|volume=168|pages=42–55|doi=10.1016/j.enbuild.2018.03.025|year=2018}}</ref><ref name=":02">{{Cite journal|last=Choi|first=Joon-Ho|title=Investigation of the correlation of building energy use intensity estimated by six building performance simulation tools|journal=Energy and Buildings|volume=147|pages=14–26|doi=10.1016/j.enbuild.2017.04.078|year=2017}}</ref> An overview about possible causes for the widely discussed [[performance gap]] from design stage to operation is given by de Wilde (2014) and a progress report by the Zero Carbon Hub (2013). Both conclude the factors mentioned above as the main uncertainties in BPS.<ref>{{Cite journal|last=de Wilde|first=Pieter|date=2014-05-01|title=The gap between predicted and measured energy performance of buildings: A framework for investigation|journal=Automation in Construction|volume=41|pages=40–49|doi=10.1016/j.autcon.2014.02.009}}</ref><ref>{{Cite web|url=http://www.zerocarbonhub.org/sites/default/files/resources/reports/Closing_the_Gap_Bewteen_Design_and_As-Built_Performance_Interim_Report.pdf|title=Closing the Gap Between Design and As-Built Performance|date=July 2013|website=www.zerocarbonhub.org|publisher=Zero Carbon Hub|access-date=2017-06-30}}</ref>

ASHRAE Standard 140-2017 "Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs (ANSI Approved)" provides a method to validate the technical capability and range of applicability of computer programs to calculate thermal performance.<ref>{{Cite book|title=ASHRAE/ANSI Standard 140-2017--Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs|last=ASHRAE|publisher=American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc.|year=2017|isbn=|___location=Atlanta, GA|pages=}}</ref> ASHRAE Guideline 4-2014 provides performance indices criteria for model calibration.<ref>{{Cite book|title=Guideline 14-2014 Measurement of Energy Demand Savings; Technical Report|last=ASHRAE|publisher=American Society of Heating, Refrigerating and Air-Conditioning Engineers.|year=2014|isbn=|___location=Atlanta, GA|pages=}}</ref> The performance indices used are normalized mean bias error (NMBE), [[coefficient of variation]] (CV) of the [[Root-mean-square deviation|root mean square error]] (RMSE), and R² ([[coefficient of determination]]). ASHRAE recommends a R² greater than 0.75 for calibrated models. The criteria for NMBE and CV RMSE depends on if measured data is available at a monthly or hourly timescale.

== Technological aspects ==

Given the complexity of building energy and mass flows, it is generally not possible to find an [[Closed-form expression|analytical solution]], so the simulation software employs other techniques, such as response function methods, or [[Numerical analysis|numerical methods]] in [[finite difference]]s or [[Finite volume method|finite volume]], as an approximation.<ref name=":0" /> Most of today's whole building simulation programs formulate models using [[imperative programming]] languages. These languages assign values to variables, declare the sequence of execution of these assignments and change the state of the program, as is done for example in [[Compatibility of C and C++|C/C++]], [[Fortran]] or [[MATLAB]]/[[Simulink]]. In such programs, model equations are tightly connected to the solution methods, often by making the solution procedure part of the actual model equations.<ref name=":22">{{Cite journal|lastlast1=Wetter|firstfirst1=Michael|last2=Bonvini|first2=Marco|last3=Nouidui|first3=Thierry S.|date=2016-04-01|title=Equation-based languages – A new paradigm for building energy modeling, simulation and optimization|journal=Energy and Buildings|volume=117|pages=290–300|doi=10.1016/j.enbuild.2015.10.017|doi-access=free}}</ref> The use of imperative programming languages limits the applicability and extensibility of models. More flexibility offer simulation engines using symbolic [[Differential-algebraic system of equations|Differential Algebraic Equations]] (DAEs) with general purpose solvers that increase model reuse, transparency and accuracy. Since some of these engines have been developed for more than 20 years (e.g. IDA ICE) and due to the key advantages of equation-based modeling, these simulation engines can be considered as [[State of the art|state of the art technology.]]<ref>{{Cite journal|lastlast1=Sahlin|firstfirst1=Per|last2=Eriksson|first2=Lars|last3=Grozman|first3=Pavel|last4=Johnsson|first4=Hans|last5=Shapovalov|first5=Alexander|last6=Vuolle|first6=Mika|date=2004-08-01|title=Whole-building simulation with symbolic DAE equations and general purpose solvers|journal=Building and Environment|series=Building Simulation for Better Building Design|volume=39|issue=8|pages=949–958|doi=10.1016/j.buildenv.2004.01.019}}</ref><ref name=":2">{{Cite journal|lastlast1=Sahlin|firstfirst1=Per|last2=Eriksson|first2=Lars|last3=Grozman|first3=Pavel|last4=Johnsson|first4=Hans|last5=Shapovalov|first5=Alexander|last6=Vuolle|first6=Mika|date=August 2003|title=Will equation-based building simulation make it?-experiences from the introduction of IDA Indoor Climate And Energy|url=https://www.academia.edu/16918862|journal=Proceedings of Building …|language=en|volume=|pages=|via=}}</ref>

== Applications ==

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* '''Building Performance Rating:''' demonstrate [[Building performance simulation#Performance-Based Compliance|performance-based compliance]] with energy codes, green certification, and financial incentives

* '''Building Stock Analysis:''' support development of energy codes and standards and plan large scale energy efficiency programs

* '''CFD in buildings:''' simulation of boundary conditions like surface heat fluxes and surface temperatures for a following [[CFD in buildings|CFD]] study of the situation<ref>{{Cite journal|lastlast1=Tian|firstfirst1=Wei|last2=Han|first2=Xu|last3=Zuo|first3=Wangda|last4=Sohn|first4=Michael D.|title=Building energy simulation coupled with CFD for indoor environment: A critical review and recent applications|journal=Energy and Buildings|volume=165|pages=184–199|doi=10.1016/j.enbuild.2018.01.046|year=2018|osti=1432688}}</ref>

== Software tools ==

There are hundreds of software tools available for simulating the performance of buildings and building subsystems, which range in capability from whole-building simulations to model input calibration to building auditing. Among whole-building simulation software tools, it is important to draw a distinction between the '''''simulation engine''''', which dynamically solves equations rooted in [[thermodynamics]] and [[building science]], and the '''''modeler application (interface)'''''.<ref name=":4" />

In general, BPS software can be classified into<ref name=":12">{{Cite journal|lastlast1=Østergård|firstfirst1=Torben|last2=Jensen|first2=Rasmus L.|last3=Maagaard|first3=Steffen E.|date=2016-08-01|title=Building simulations supporting decision making in early design – A review|journal=Renewable and Sustainable Energy Reviews|volume=61|pages=187–201|doi=10.1016/j.rser.2016.03.045|url=https://vbn.aau.dk/da/publications/b5c1c030-0ce9-493d-89ef-36d2382e432a}}</ref>

* Applications with integrated simulation engine (e.g. EnergyPlus, ESP-r, TAS, IES-VE, IDA ICE)

* Software that docks to a certain engine (e.g. [[Designbuilder]], eQuest, RIUSKA, Sefaira)

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|TAS 3D Modeler

|-

|TRNSYS<ref name=":7">{{Cite journal|lastlast1=Beckman|firstfirst1=William A.|last2=Broman|first2=Lars|last3=Fiksel|first3=Alex|last4=Klein|first4=Sanford A.|last5=Lindberg|first5=Eva|last6=Schuler|first6=Mattias|last7=Thornton|first7=Jeff|title=TRNSYS The most complete solar energy system modeling and simulation software|journal=Renewable Energy|language=en|volume=5|issue=1–4|pages=486–488|doi=10.1016/0960-1481(94)90420-0|year=1994}}</ref>

|[[University of Wisconsin–Madison|University of Wisconsin-Madison]], US

|1975