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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|last=Mattsson|first=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>
* Better concept promotion
* Standardization of input data and accessibility of model libraries
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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|last=Raftery|first=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|last=Heo|first=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|last=Coakley|first=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|last=Li|first=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|last=Hong|first=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|last=Mustafaraj|first=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|last=Christensen|first=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|last=Cornaro|first=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|last=Cornaro|first=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|last=Dodoo|first=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|last=Imam|first=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|last=Nageler|first=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 R<sup>2</sup> ([[coefficient of determination]]). ASHRAE recommends a R<sup>2</sup> 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|last=Wetter|first=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|last=Sahlin|first=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|last=Sahlin|first=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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