A review of methods to match building energy simulation models to measured data

Renewable and Sustainable Energy Reviews 37 (2014) 123–141 Contents lists available at ScienceDirect Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser A review of methods to match building energy simulation models to measured data Daniel Coakley a,b, n , Paul Raftery c , Marcus Keane a,b a Department of Civil Engineering, NUI Galway, Ireland Informatics Research Unit for Sustainable Engineering (IRUSE), NUI Galway, Ireland c Centre for the Built Environment (CBE), University of California, Berkeley, United States b art ic l e i nf o a b s t r a c t Article history: Received 19 March 2014 Accepted 3 May 2014 Whole building energy simulation (BES) models play a significant role in the design and optimisation of buildings. Simulation models may be used to compare the cost-effectiveness of energy-conservation measures (ECMs) in the design stage as well as assessing various performance optimisation measures during the operational stage. However, due to the complexity of the built environment and prevalence of large numbers of independent interacting variables, it is difficult to achieve an accurate representation of real-world building operation. Therefore, by reconciling model outputs with measured data, we can achieve more accurate and reliable results. This reconciliation of model outputs with measured data is known as calibration. This paper presents a detailed review of current approaches to model development and calibration, highlighting the importance of uncertainty in the calibration process. This is accompanied by a detailed assessment of the various analytical and mathematical/statistical tools employed by practitioners to date, as well as a discussion on both the problems and the merits of the presented approaches. & 2014 Elsevier Ltd. All rights reserved. Keywords: Review Calibration Optimisation Simulation EnergyPlus Uncertainty Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Building energy performance simulation (BEPS) tools. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Benefits of BEPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2. Problems with BEPS and model calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 3. Methods for assessing calibration performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 4. Uncertainty in building simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 5. Current approaches to BEPS calibration: . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Analytical tools and techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Mathematical/statistical techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Summary of manual calibration developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Characterisation techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Advanced graphical approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Procedural extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Summary of automated calibration developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Optimisation techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Alternative modelling techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 7. Graphical summary of reviewed papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Appendix A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 n Corresponding author at: Department of Civil Engineering, NUI Galway, Ireland. Tel.: þ 353 87 2285147. E-mail address: daniel.coakley@nuigalway.ie (D. Coakley). http://dx.doi.org/10.1016/j.rser.2014.05.007 1364-0321/& 2014 Elsevier Ltd. All rights reserved.

[1]  Jeff Haberl,et al.  Predicting hourly building energy usage , 1994 .

[2]  Paul Raftery,et al.  VISUALIZING PATTERNS IN BUILDING PERFORMANCE DATA , 2011 .

[3]  Kevin J. Lomas,et al.  Sensitivity analysis techniques for building thermal simulation programs , 1992 .

[4]  T. Agami Reddy,et al.  Literature review on calibration of building energy simulation programs : Uses, problems, procedures, uncertainty, and tools , 2006 .

[5]  Paul Raftery,et al.  CALIBRATION OF A DETAILED BES MODEL TO MEASURED DATA USING AN EVIDENCE-BASED ANALYTICAL OPTIMISATION APPROACH , 2011 .

[6]  Massimiliano Manfren,et al.  Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation , 2013 .

[7]  Cathy Turner,et al.  Green Building Performance Evaluation: Measured Results from LEED-New Construction Buildings , 2008 .

[8]  Paul Strachan,et al.  Practical application of uncertainty analysis , 2001 .

[9]  Simeng Liu,et al.  CALIBRATION OF BUILDING MODELS FOR SUPERVISORY CONTROL OF COMMERCIAL BUILDINGS , 2005 .

[10]  T. Agami Reddy,et al.  An Evaluation of Classical Steady-State Off-Line Linear Parameter Estimation Methods Applied to Chiller Performance Data , 2002 .

[11]  J A Clarke,et al.  AN APPROACH TO THE CALIBRATION OF BUILDING ENERGY SIMULATION MODELS , 1993 .

[12]  Paul Raftery,et al.  Calibrating whole building energy models: An evidence-based methodology , 2011 .

[13]  L. K. Norford,et al.  Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model , 1994 .

[14]  A. OHagan,et al.  Bayesian analysis of computer code outputs: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[15]  B. D. Hunn,et al.  Comparison of DOE-2 computer program simulations to metered data for seven commercial buildings , 1981 .

[16]  D.G. Dudley,et al.  Dynamic system identification experiment design and data analysis , 1979, Proceedings of the IEEE.

[17]  Godfried Augenbroe,et al.  Analysis of uncertainty in building design evaluations and its implications , 2002 .

[18]  Keith Beven,et al.  Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data , 1998 .

[19]  R. Judkoff,et al.  Applying the building energy simulation test (BESTEST) diagnostic method to verification of space conditioning equipment models used in whole-building energy simulation programs , 2002 .

[20]  Yeonsook Heo,et al.  Calibration of building energy models for retrofit analysis under uncertainty , 2012 .

[21]  Mark Stetz,et al.  M&v guidelines: measurement and verification for federal energy projects, version 2.2 , 2000 .

[22]  Aris Tsangrassoulis,et al.  On the energy consumption in residential buildings , 2002 .

[23]  P. Young,et al.  Simplicity out of complexity in environmental modelling: Occam's razor revisited. , 1996 .

[24]  J. P. Waltz.  Practical experience in achieving high levels of accuracy in energy simulations of existing buildings , 1995 .

[25]  Fredrik Karlsson,et al.  Measured and predicted energy demand of a low energy building: important aspects when using Building Energy Simulation , 2007 .

[26]  D. C. Hittle,et al.  Calibrating building energy analysis models using short term test data , 1996 .

[27]  Jos Van Orshoven.  Possibilities and limitations of the SWA-Tool for the assessment of the impact of farming practices. Scientific report. Joint Research Centre of the European Commission, Institute for Environment and Sustainability, Ispra, Italy , 2005 .

[28]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[29]  John H. Scofield,et al.  Do LEED-certified buildings save energy? Not really… , 2009 .

[30]  R. Rosen.  Life Itself: A Comprehensive Inquiry Into the Nature, Origin, and Fabrication of Life , 1991 .

[31]  Paul Raftery,et al.  CALIBRATION OF WHOLE BUILDING ENERGY SIMULATION MODELS: DETAILED CASE STUDY OF A NATURALLY VENTILATED BUILDING USING HOURLY MEASURED DATA , 2012 .

[32]  Clarke J A Macdonald I A,et al.  ASSESSING UNCERTAINTY IN BUILDING SIMULATION , 1999 .

[33]  Paul Raftery,et al.  Calibrating whole building energy models: Detailed case study using hourly measured data , 2011 .

[34]  Jian Sun,et al.  Calibration of Building Energy Simulation Programs Using the Analytic Optimization Approach (RP-1051) , 2006 .

[35]  H. Akbari,et al.  Application of an End-Use Disaggregation Algorithm for Obtaining Building Energy-Use Data , 1998 .

[36]  Michael J. Witte,et al.  Analytical and comparative testing of EnergyPlus using IEA HVAC BESTEST E100-E200 test suite , 2004 .

[37]  John Haymaker,et al.  Formalizing Approximations , Assumptions , and Simplifications to Document Limitations in Building Energy Performance Simulation , 2011 .

[38]  Isaac Turiel,et al.  Simplified energy analysis methodology for commercial buildings , 1984 .

[39]  D. N. Asimakopoulos,et al.  Modelling the earth temperature using multiyear measurements , 1992 .

[40]  K. Subbarao,et al.  PSTAR: Primary and secondary terms analysis and renormalization: A unified approach to building energy simulations and short-term monitoring , 1988 .

[41]  Emily M. Ryan,et al.  Validation of building energy modeling tools under idealized and realistic conditions , 2012 .

[42]  David E. Claridge,et al.  Simplified Building and Air-handling Unit Model Calibration and Applications , 2004 .

[43]  Jeff Haberl,et al.  Procedures for Calibrating Hourly Simulation Models to Measured Building Energy and Environmental Data , 1998 .

[44]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part I: General Methodology (RP-1051) , 2007 .

[45]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[46]  Hyeun Jun Moon,et al.  Assessing Mold Risks in Buildings under Uncertainty , 2009 .

[47]  Carlos Christensen.  Digital and color energy maps for graphic display of hourly data , 1984 .

[48]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[49]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[50]  David E. Claridge,et al.  Manual of Procedures for Calibrating Simulations of Building Systems , 2003 .

[51]  Veronica I. Soebarto.  CALIBRATION OF HOURLY ENERGY SIMULATIONS USING HOURLY MONITORED DATA AND MONTHLY UTILITY RECORDS FOR TWO CASE STUDY BUILDINGS , 1997 .

[52]  Albert Thumann.  Handbook of Energy Audits , 1979 .

[53]  Mustafa Abbas.  Development of Graphical Indices for Building Energy Data , 1993 .

[54]  Shirley Hansen,et al.  Investment Grade Energy Audit , 2003 .

[55]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[56]  Xiufeng Pang,et al.  Uncertainties in Energy Consumption Introduced by Building Operations and Weather for a Medium-Size Office Building , 2012 .

[57]  J. J. Hirsch,et al.  DOE-2 supplement: Version 2.1E , 1993 .

[58]  Vineeta Pal,et al.  RESEM-CA: Validation and testing , 2002 .

[59]  V. Geros,et al.  Modeling and predicting building's energy use with artificial neural networks: Methods and results , 2006 .

[60]  Zheng O'Neill,et al.  Uncertainty and sensitivity decomposition of building energy models , 2012 .

[61]  B. D. Hunn,et al.  Energy Analysis of the Texas Capitol Restoration , 1992 .

[62]  Mingsheng Liu,et al.  A rapid calibration procedure and case study for simplified simulation models of commonly used HVAC systems , 2011 .

[63]  Jeff Haberl,et al.  Exploring new techniques for displaying complex building energy consumption data , 1996 .

[64]  Soteris A. Kalogirou,et al.  Artificial neural networks for the prediction of the energy consumption of a passive solar building , 2000 .

[65]  Jeff Haberl,et al.  Graphical tools to help calibrate the DOE-2 simulation program , 1993 .

[66]  Robert J. Hitchcock,et al.  RESEM: Retrofit Energy Savings Estimation Model reference manual - Version 1.00 , 1991 .

[67]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[68]  M. Liu,et al.  Application of calibrated HVAC system models to identify component malfunctions and to optimize the operation and control schedules , 1995 .

[69]  Andreea Mihai.  Calibration of a Building Energy Model Using Measured Data for a Research Center , 2014 .

[70]  Pieter de Wilde,et al.  Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change , 2009 .

[71]  Kevin J. Lomas,et al.  Empirical validation of building energy simulation programs , 1997 .

[72]  D. E. Claridge,et al.  Signatures of Heating and Cooling Energy Consumption for Typical AHUs , 1998 .

[73]  David E. Claridge,et al.  Use of Simplified System Models to Measure Retrofit Energy Savings , 1993 .

[74]  David J. Spiegelhalter,et al.  A hierarchical Bayesian framework for calibrating micro-level models with macro-level data , 2013 .

[75]  F. W. Yu,et al.  Energy signatures for assessing the energy performance of chillers , 2005 .

[76]  M. Liu,et al.  Calibrating AHU models using whole building cooling and heating energy consumption data , 1998 .

[77]  Roberto Lamberts,et al.  BUILDING SIMULATION CALIBRATION USING SENSITIVITY ANALYSIS , 2005 .

[78]  David J. C. MacKay,et al.  BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION , 1996 .

[79]  Godfried Augenbroe,et al.  Trends in building simulation , 2002 .

[80]  N Oreskes,et al.  Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences , 1994, Science.

[81]  Alberto Hernandez Neto,et al.  Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption , 2008 .

[82]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part II: Application to Three Case Study Office Buildings (RP-1051) , 2007 .

[83]  Karine Lavigne,et al.  ASSISTED CALIBRATION IN BUILDING SIMULATION-ALGORITHM DESCRIPTION AND CASE STUDIES , 2009 .

[84]  David E. Claridge,et al.  Use of Calibrated HVAC System Models to Optimize System Operation , 1998 .

[85]  Li Shao,et al.  A CALIBRATED WHOLE BUILDING SIMULATION APPROACH TO ASSESSING RETROFIT OPTIONS FOR BIRMINGHAM AIRPORT , 2012 .

[86]  Jerome R. Ravetz,et al.  Uncertainty and Quality in Science for Policy , 1990 .

[87]  Joseph C. Lam,et al.  Sensitivity analysis of energy performance of office buildings , 1996 .

[88]  Hashem Akbari,et al.  Validation of an algorithm to disaggregate whole-building hourly electrical load into end uses , 1995 .

[89]  David E. Claridge,et al.  Development of an Inverse Method to Estimate Overall Building and Ventilation Parameters of Large Commercial Buildings , 1999 .

[90]  J. F. Kreider.  Prediction Hourly Building Energy Use : The Great Energy Predictor Shootout - Overview and Discussion of Results , 1994 .

[91]  Zheng O'Neill,et al.  A methodology for meta-model based optimization in building energy models , 2012 .

[92]  Euy-Joon Lee,et al.  CALIBRATION PROCEDURE OF ENERGY PERFORMANCE SIMULATION MODEL FOR A COMMERCIAL BUILDING , 1999 .

[93]  Donald L. Hadley,et al.  Daily variations in HVAC system electrical energy consumption in response to different weather conditions , 1993 .

[94]  Jeff Haberl,et al.  Development of Graphical Indices for Viewing Building Energy Data: Part II , 1998 .

[95]  Stefano Tarantola,et al.  Global Sensitivity Analysis: An Introduction , 2005 .

[96]  Andrea Saltelli,et al.  Sensitivity Analysis for Importance Assessment , 2002, Risk analysis : an official publication of the Society for Risk Analysis.

[97]  H. Madsen,et al.  Modelling the heat consumption in district heating systems using a grey-box approach , 2006 .

[98]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[99]  David E. Claridge,et al.  Calibration Procedure for Energy Performance Simulation of a Commercial Building , 2003 .

[100]  T. E. Bou-Saada,et al.  An Improved Procedure for Developing Calibrated Hourly Simulation Models , 1995 .