Building Simulation

, Volume 6, Issue 4, pp 365–377 | Cite as

Leveraging the analysis of parametric uncertainty for building energy model calibration

  • Zheng O’Neill
  • Bryan Eisenhower
Research Article Building Thermal, Lighting, and Acoustics Modeling


Calibrated energy models are used for measurement and verification of building retrofit projects, predictions of savings from energy conservation measures, and commissioning building systems (both prior to occupancy and during real-time model based performance monitoring, controls and diagnostics). This paper presents a systematic and automated way to calibrate a building energy model. Efficient parameter sampling is used to analyze more than two thousand model parameters and identify which of these are critical (most important) for model tuning. The parameters that most affect the building’s energy end-use are selected and automatically refined to calibrate the model by applying an analytic meta-model based optimization. Real-time data from an office building, including weather and energy meter data in 2010, was used for the model calibration, while 2011 data was used for the model verification. The modeling process, calibration and verification results, as well as implementation issues encountered throughout the model calibration process from a user’s perspective are discussed. The total facility and plug electricity consumption predictions from the calibrated model match the actual measured monthly data within ±5%. The calibrated model gives 2.80% of Coefficient of Variation of Root Mean Squared Error (CV (RMSE)) and −2.31% of Normalized Mean Bias Error (NMBE) for the whole building monthly electricity use, which is acceptable based on the ASHRAE Guideline 14–2002. In this work we use EnergyPlus as a modeling tool, while the method can be used with other modeling tools equally as well.


EnergyPlus calibration sensitivity analysis meta-model based optimization 


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  1. Aimdyn GoSUM Software (2010). Global Optimization, Sensitivity and Uncertainty in Models (GoSUM). Santa Barbara, USA: Aimdyn Inc. Available: Accessed Aug. 2010.Google Scholar
  2. ASHRAE (2002). ASHRAE Guideline 14-2002: Measurement of Energy Demand and Savings. Atlanta, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  3. ASHRAE (2010). ASHRAE Standard 90.1-2010 Energy Standard for Buildings Except Low-Rise Residential Buildings.Google Scholar
  4. Claridge DE (2004). Using simulation models for building commissioning. In: Proceedings of 4th International Conference for Enhanced Building Operations, Paris, France.Google Scholar
  5. DesignBuilder (2010). DesignBuilder 2.0. Avaialbe: Scholar
  6. DOE (2012). 2011 Building Energy Data Book. Available: Accessed Nov. 2012.Google Scholar
  7. Dudley JH, Black D, Apte M, Piette MA, Berkeley P (2010). Comparison of demand response performance with an energyplus model in a low energy campus building. In: Proceedings of ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, USA.Google Scholar
  8. Eisenhower B, O’Neill Z, Narayanan S, Fonoberov VA, Mezic I (2012). A methodology for meta-model based optimization in building energy models. Energy and Buildings, 47: 292–301.CrossRefGoogle Scholar
  9. Eisenhower B, O’Neill Z, Fonoberov VA, Mezic I (2011). Uncertainty and sensitivity decomposition of building energy models. Journal of Building Performance Simulation, 5: 171–184.CrossRefGoogle Scholar
  10. EnergyPlus (2011). EnergyPlus 6.0. Available: Scholar
  11. Firth S, Lomas K, Wright A (2010). Targeted household energy- efficiency measures using sensitivity analysis. Building Research and Information, 38: 25–41.CrossRefGoogle Scholar
  12. Haves P, Salsbury T, Claridge D, Liu M (2001). Use of whole building simulation in on-line performance assessment: Modeling and implementation issues. In: Proceedings of 7th International IBPSA Conference, Rio de Janeiro, Brazil.Google Scholar
  13. Keranen H, Suur-Uski T, Vuolle M (2007). Calibration of building simulation model by using building automation system-A case study. In: Proceedings of CLIMA 2007-WellBeing Indoors. Helsinki, Finland.Google Scholar
  14. Li G, Wang S-W, Rabitz H, Wang S, Jaffe P (2002). Global uncertainty assessments by high dimensional model representations (HDMR). Chemical Engineering Science, 57: 4445–4460.CrossRefGoogle Scholar
  15. Liu G, Liu M (2011). A rapid calibration procedure and case study for simplified simulation models of commonly used HVAC systems. Building and Environment, 46: 409–420.CrossRefGoogle Scholar
  16. Liu M, Claridge DE, Bensouda N, Heinemeier K, Uk Lee S, Wei G (2003). High performance commercial building systems: Manual of procedures for calibrating simulations of building systems. Lawrence Berkeley National Laboratory. Available: http// Scholar
  17. Mara T, Tarantola S (2008). Application of global sensitivity analysis of model output to building thermal simulations. Building Simulation, 1: 290–302.CrossRefGoogle Scholar
  18. O’Neill Z, Shashanka M, Pang X, Bailey T, Haves P (2011a). Real Time model-based energy diagnostics in buildings. In: Proceedings of 12th International IBPSA Conference, Sydney, Australia.Google Scholar
  19. O’Neill Z, Eisenhower B, Yuan S, Bailey T, Narayanan S, Fonoberov V (2011b). Modeling and calibration of energy models for a DoD building. ASHRAE Transactions, 117(2): 358–365.Google Scholar
  20. Pan Y, Huang Z, Wu G, Chen C (2006). The application of building energy simulation and calibration in two high-rise commercial buildings in Shanghai. In: Proceedings 2nd National Conference of IBPSA-USA: SimBuild, Cambridge, MA, USA.Google Scholar
  21. Pollack M, Roderick Y, McEwan D, Wheatley C (2009). Building simulation as an assisting tool in designing an energy efficient building: A case study. In: Proceedings of 11th International IBPSA Conference (pp. 1191–11980), Glasgow, Scotland, UK.Google Scholar
  22. Raftery P, Keane M, O’Donnell J (2011a). Calibrating whole building energy models: An evidence-based methodology. Energy and Buildings, 43: 2356–2364.CrossRefGoogle Scholar
  23. Raftery P, Keane M, O’Donnell J (2011b). Calibrating whole building energy models: Detailed case study using hourly measured data. Energy and Buildings, 43: 3666–3679.CrossRefGoogle Scholar
  24. Rahni N, Ramdani N, Candau Y, Dalicieux P (1997). Application of group screening to dynamic building energy simulation models. Journal of Statistical Computation and Simulation, 57: 285–304.CrossRefzbMATHGoogle Scholar
  25. Ramirez R, Sebold F, Mayer T, Ciminelli M, Abrishami M (2005). A building simulation palooza: The California CEUS project and DrCEUS. In: Proceedings of 9th International IBPSA Conference, Montreal, Canada.Google Scholar
  26. Reddy TA, Maor I, Sun J, Panjapornpon C (2006). ASHRAE Research Project 1051-RP Final Report: Procedures for Reconciling Computer-Calculated Results with Measured Energy Data.Google Scholar
  27. Reddy TA (2006). Literature review on calibration of building energy simulation programs: Uses, problems, procedures, uncertainty and tools. ASHRAE Transactions, 112(1): 226–240.Google Scholar
  28. Saltelli A, Chan K, Scott EM (2000). Sensitivity Analysis. West Sussex, UK: John Wiley & Sons.zbMATHGoogle Scholar
  29. Sobol I, Kucherenko S (2009). Derivative based global sensitivity measures and their link with global sensitivity indices. Mathematics and Computers in Simulation, 79: 3009–3017.MathSciNetCrossRefzbMATHGoogle Scholar
  30. Spitler, J, Fisher D, Zietlow D (1989). A primer on the use of influence coefficients in building simulation. In: Proceedings of International IBPSA Conference (pp. 299–304), Vancouver, Canada.Google Scholar
  31. Struck C, Hensen J, Kotek P (2009). On the application of uncertainty and sensitivity analysis with abstract building performance simulation tools. Journal of Building Physics, 33: 5–27.CrossRefGoogle Scholar
  32. Sun J, Reddy TA, 2006. Calibration of building energy simulation programs using the analytical optimization approach (RP-1051). HVAC&R Research, 12: 177–196.CrossRefGoogle Scholar
  33. USGBC (2010). US Green Building Council, LEED for New Construction & Major Renovations v3.0. Available: Scholar
  34. Venancio R, Pedrini A (2009). The influence of design decisions on energy consumption and thermal performance: The case of UFRN Campus, Brazil. In: Proceedings of 11th International IBPSA Conference (pp. 136–143), Glasgow, Scotland, UK.Google Scholar
  35. Wetter M, Wright J (2003). Comparison of a generalized pattern search and a genetic algorithm optimization method. In: Proceedings of 8th International IBPSA Conference (pp. 1401–1408), Eindhoven, Netherlands.Google Scholar
  36. Wilcox S, Marion W (2008). Users Manual for TMY3 Data Sets. NREL Technical Report: NREL/TP-581-43156.Google Scholar
  37. Yoon J, Lee EJ, Claridge DE (2003). Calibration procedure for energy performance simulation of a commercial building. Journal of Solar Energy Engineering, 125: 251–257.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.United Technologies Research CenterEast HartfordUSA
  2. 2.University of CaliforniaSanta BarbaraUSA

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