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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|last1=Raftery|first1=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}}</ref><ref>{{Cite journal|last1=Heo|first1=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|last1=Coakley|first1=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|last1=Li|first1=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|last1=Hong|first1=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|last1=Mustafaraj|first1=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|last1=Christensen|first1=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|last1=Cornaro|first1=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|last1=Cornaro|first1=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|last1=Dodoo|first1=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|last1=Imam|first1=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|s2cid=55153560|url=http://opus.bath.ac.uk/53934/1/ImamColeyWalker2017.pdf}}</ref> and on the applied methods in the simulation engine.<ref>{{Cite journal|last1=Nageler|first1=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|s2cid=117446952 }}</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|___location=Atlanta, GA}}</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|___location=Atlanta, GA}}</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.
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* '''Building Performance Rating:''' demonstrate [[#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|last1=Tian|first1=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|url=https://scholar.colorado.edu/concern/articles/q811kk637 }}</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|last1=Østergård|first1=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|s2cid=56153278 |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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