Evaluation of calibration efficacy under different levels of uncertainty

This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation of the accuracy of calibration results in terms of both calibrated parameter values and model predictions. Case studies demonstrate the performance of Bayesian calibration of base models developed from audit data with differing levels of detail in building design, usage, and operation.

[1]  Wim Schoutens,et al.  Enhancing the Morris Method , 2005 .

[2]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[3]  Ron Smith,et al.  Bayesian calibration of process-based forest models: bridging the gap between models and data. , 2005, Tree physiology.

[4]  G. D. Wyss,et al.  A user`s guide to LHS: Sandia`s Latin Hypercube Sampling Software , 1998 .

[5]  Godfried Augenbroe,et al.  Uncertainty quantification of microclimate variables in building energy simulation , 2011 .

[6]  Ardeshir Mahdavi,et al.  A CASE STUDY OF OPTIMIZATION-AIDED THERMAL BUILDING PERFORMANCE SIMULATION CALIBRATION , 2013 .

[7]  Oar,et al.  International Performance Measurement and Verification Protocol , 2014 .

[8]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

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

[10]  S. Kitazawa,et al.  Bayesian calibration of simultaneity in tactile temporal order judgment , 2006, Nature Neuroscience.

[11]  David E. Claridge,et al.  ASHRAE's Guideline 14-2002 for Measurement of Energy and Demand Savings: How to Determine What Was Really Saved by the Retrofit , 2005 .

[12]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[13]  Gang Wu,et al.  Calibrated building energy simulation and its application in a high-rise commercial building in Shanghai , 2007 .

[14]  Huafen Hu,et al.  Risk-conscious design of off-grid solar energy houses , 2009 .

[15]  Yeonsook Heo,et al.  Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty , 2011 .

[16]  Yimin Zhu,et al.  Applying computer-based simulation to energy auditing: A case study , 2006 .

[17]  Huafen Hu,et al.  Building Energy Benchmarking for Retrofit: An Application of Normative Building Modeling , 2012 .

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

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

[20]  Wei Tian,et al.  A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London , 2012 .

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

[22]  François Garde,et al.  Bayesian Parameter Estimation of Convective Heat Transfer Coefficients of a Roof-Mounted Radiant Barrier System , 2006 .

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

[24]  Mark S. Martinez,et al.  International performance measurement & verification protocol: Concepts and options for determining energy and water savings , 2001 .

[25]  D A Henderson,et al.  Bayesian Calibration of a Stochastic Kinetic Computer Model Using Multiple Data Sources , 2010, Biometrics.

[26]  Sang Hoon Lee,et al.  SCALABLE METHODOLOGY FOR ENERGY EFFICIENCY RETROFIT DECISION ANALYSIS , 2012 .

[27]  Dave Higdon,et al.  Combining Field Data and Computer Simulations for Calibration and Prediction , 2005, SIAM J. Sci. Comput..

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

[29]  Daniel Tarlow,et al.  AUTOMATICALLY CALIBRATING A PROBABILISTIC GRAPHICAL MODEL OF BUILDING ENERGY CONSUMPTION , 2009 .

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

[31]  Diane J. Graziano,et al.  Testing a Streamlined Project Evaluation Tool for Risk-Conscious Decision Making: The Chicago Loop Energy Efficiency Retrofit Initiative , 2012 .

[32]  Fei Zhao,et al.  Agent-based modeling of commercial building stocks for energy policy and demand response analysis , 2012 .

[33]  Andrew K. Persily,et al.  Analysis of Ventilation Data from the U.S. Environmental Protection Agency Building Assessment Survey and Evaluation (BASE) Study , 2004 .

[34]  Yeonsook Heo,et al.  Quantitative risk management for energy retrofit projects , 2013 .

[35]  Godfried Augenbroe,et al.  Uncertainty quantification of microclimate variables in building energy models , 2014 .

[36]  S. Chib,et al.  Understanding the Metropolis-Hastings Algorithm , 1995 .

[37]  Max D. Morris,et al.  Factorial sampling plans for preliminary computational experiments , 1991 .

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

[39]  Stefano Tarantola,et al.  Application of global sensitivity analysis of model output to building thermal simulations , 2008 .

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

[41]  M. S. De Wit,et al.  Uncertainty in predictions of thermal comfort in buildings , 2001 .

[42]  Christiaan J. J. Paredis,et al.  TOWARDS BETTER PREDICTION OF BUILDING PERFORMANCE: A WORKBENCH TO ANALYZE UNCERTAINTY IN BUILDING SIMULATION , 2013 .

[43]  Tiangang Cui,et al.  Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm , 2011 .

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

[45]  A. Pedrinia,et al.  A methodology for building energy modelling and calibration in warm climates , 2002 .