Influence of error terms in Bayesian calibration of energy system models

References

• Arendt, Paul D., Daniel W. Apley, and Wei Chen. 2012. “Quantification of Model Uncertainty: Calibration, Model Discrepancy, and Identifiability.” Journal of Mechanical Design 134 (10): 100908. doi: 10.1115/1.4007390
   Web of Science ®Google Scholar
• ASHRAE. 2002. ASHRAE Guideline 14, Measurement of Energy and Demand Savings. Atlanta.
   Google Scholar
• Bayarri, Maria J., James O. Berger, Rui Paulo, Jerry Sacks, John A. Cafeo, James Cavendish, Chin-Hsu Lin, and Jian Tu. 2007. “A Framework for Validation of Computer Models.” Technometrics 49: 138–154. doi: 10.1198/004017007000000092
   Web of Science ®Google Scholar
• Berger, James O., Elías Moreno, Luis Raul Pericchi, M. Jesús Bayarri, José M. Bernardo, Juan A. Cano, Julián De la Horra, et al. 1994. “An Overview of Robust Bayesian Analysis.” Test 3 (1): 5–124. doi: 10.1007/BF02562676
   Google Scholar
• Betancourt, Michael. 2016. “A Conceptual Introduction to Hamiltonian Monte Carlo.” 60.
   Google Scholar
• Booth, A. T., R. Choudhary, and D. J. Spiegelhalter. 2013. “A Hierarchical Bayesian Framework for Calibrating Micro-Level Models with Macro-Level Data.” Journal of Building Performance Simulation 6 (4): 293–318. doi: 10.1080/19401493.2012.723750
   Web of Science ®Google Scholar
• Brynjarsdóttir, Jenný, and Anthony O'Hagan. 2014. “Learning about Physical Parameters: The Importance of Model Discrepancy.” Inverse Problems 30 (11): 114007. doi: 10.1088/0266-5611/30/11/114007
   Web of Science ®Google Scholar
• Cacabelos, Antón, Pablo Eguía, José Luís Míguez, Enrique Granada, and Maria Elena Arce. 2015. “Calibrated Simulation of a Public Library HVAC System with a Ground-Source Heat Pump and a Radiant Floor Using TRNSYS and GenOpt.” Energy and Buildings 108: 114–126. doi: 10.1016/j.enbuild.2015.09.006
   Web of Science ®Google Scholar
• Chong, Adrian, and Khee Poh Lam. 2017. “A Comparison of MCMC Algorithms for the Bayesian Calibration of Building Energy Models.” In Building Simulation 2017, 1–10. San Francisco: Conference Proceedings.
   Google Scholar
• Chong, Adrian, Khee Poh Lam, Matteo Pozzi, and Junjing Yang. 2017. “Bayesian Calibration of Building Energy Models with Large Datasets.” Energy and Buildings 154: 343–355. doi: 10.1016/j.enbuild.2017.08.069
   Web of Science ®Google Scholar
• Coakley, Daniel, Paul Raftery, and Marcus Keane. 2014. “A Review of Methods to Match Building Energy Simulation Models to Measured Data.” Renewable and Sustainable Energy Reviews 37 (0): 123–141. doi: 10.1016/j.rser.2014.05.007
   Google Scholar
• Cobelli, Claudio, and Joseph J. DiStefano. 1980. “Parameter and Structural Identifiability Concepts and Ambiguities: A Critical Review and Analysis.” American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 239 (1): R7–R24. doi: 10.1152/ajpregu.1980.239.1.R7
   Google Scholar
• Collis, Joe, Anthony J. Connor, Marcin Paczkowski, Pavitra Kannan, Joe Pitt-Francis, Helen M. Byrne, and Matthew E. Hubbard. 2017. “Bayesian Calibration, Validation and Uncertainty Quantification for Predictive Modelling of Tumour Growth: A Tutorial.” Bulletin of Mathematical Biology, 1–36. doi: 10.1007/s11538-017-0258-5.
   Web of Science ®Google Scholar
• Duane, Simon, A. D. Kennedy, Brian J. Pendleton, and Duncan Roweth. 1987. “Hybrid Monte Carlo.” Physics Letters B 195 (2): 216–222. doi: 10.1016/0370-2693(87)91197-X
   Web of Science ®Google Scholar
• Fabrizio, Enrico, and Valentina Monetti. 2015. “Methodologies and Advancements in the Calibration of Building Energy Models.” Energies 8 (4): 2548–2574. doi: 10.3390/en8042548
   Web of Science ®Google Scholar
• Faggianelli, Ghjuvan Antone, Laurent Mora, and Rania Merheb. 2017. “Uncertainty Quantification for Energy Savings Performance Contracting: Application to an Office Building.” Energy and Buildings 152: 61–72. doi: 10.1016/j.enbuild.2017.07.022
   Web of Science ®Google Scholar
• Fisher, Daniel E., Simon J. Rees, S. K. Padhmanabhan, and A. Murugappan. 2006. “Implementation and Validation of Ground-Source Heat Pump System Models in an Integrated Building and System Simulation Environment.” HVAC&R Research 12 (supp1): 693–710. doi: 10.1080/10789669.2006.10391201
   Google Scholar
• Freni, Gabriele, and Giorgio Mannina. 2010. “Bayesian Approach for Uncertainty Quantification in Water Quality Modelling: The Influence of Prior Distribution.” Journal of Hydrology 392 (1–2): 31–39. doi: 10.1016/j.jhydrol.2010.07.043
   Web of Science ®Google Scholar
• Gelman, Andrew, John B. Carlin, Hal S. Stern, and Donald B. Rubin. 2014. Bayesian Data Analysis. Vol. 2. Boca Raton, FL: Chapman & Hall/CRC.
   Google Scholar
• Gelman, Andrew, and Donald B Rubin. 1992. “Inference from Iterative Simulation Using Multiple Sequences.” Statistical Science 7: 457–472. doi: 10.1214/ss/1177011136
   Google Scholar
• Gestwick, Michael J., and James A. Love. 2014. “Trial Application of ASHRAE 1051-RP: Calibration Method for Building Energy Simulation.” Journal of Building Performance Simulation 7 (5): 346–359. doi: 10.1080/19401493.2013.838698
   Web of Science ®Google Scholar
• Guillas, S., J. Rougier, A. Maute, A. D. Richmond, and C. D. Linkletter. 2009. “Bayesian Calibration of the Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM).” Geoscientific Model Development 2 (2): 137–144. doi: 10.5194/gmd-2-137-2009
   Web of Science ®Google Scholar
• Heo, Yeonsook, Ruchi Choudhary, and G. A. Augenbroe. 2012. “Calibration of Building Energy Models for Retrofit Analysis Under Uncertainty.” Energy and Buildings 47: 550–560. doi: 10.1016/j.enbuild.2011.12.029
   Web of Science ®Google Scholar
• Heo, Yeonsook, Diane J. Graziano, Leah Guzowski, and Ralph T. Muehleisen. 2014. “Evaluation of Calibration Efficacy Under Different Levels of Uncertainty.” Journal of Building Performance Simulation, 1–10. doi:10.1080/19401493.2014.896947.
   Web of Science ®Google Scholar
• Higdon, Dave, Marc Kennedy, James C. Cavendish, John A. Cafeo, and Robert D. Ryne. 2004. “Combining Field Data and Computer Simulations for Calibration and Prediction.” SIAM Journal on Scientific Computing 26 (2): 448–466. doi: 10.1137/S1064827503426693
   Web of Science ®Google Scholar
• Hoffman, Matthew D., and Andrew Gelman. 2014. “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo.” Journal of Machine Learning Research 15 (1): 1593–1623.
   Google Scholar
• Hopfe, Christina J., and Jan L. M. Hensen. 2011. “Uncertainty Analysis in Building Performance Simulation for Design Support.” Energy and Buildings 43 (10): 2798–2805. doi: 10.1016/j.enbuild.2011.06.034
   Web of Science ®Google Scholar
• Kavetski, Dmitri, George Kuczera, and Stewart W. Franks. 2006a. “Bayesian Analysis of Input Uncertainty in Hydrological Modeling: 2. Application.” Water Resources Research 42 (3): W03408. doi:doi: 10.1029/2005WR004376
   Web of Science ®Google Scholar
• Kavetski, Dmitri, George Kuczera, and Stewart W. Franks. 2006b. “Bayesian Analysis of Input Uncertainty in Hydrological Modeling: 1. Theory.” Water Resources Research 42 (3): W03407. doi: 10.1029/2005WR004368.
   Web of Science ®Google Scholar
• Kennedy, Marc C., and Anthony O’Hagan. 2001. “Bayesian Calibration of Computer Models.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 63 (3): 425–464. doi: 10.1111/1467-9868.00294
   Web of Science ®Google Scholar
• Li, Qi, Godfried Augenbroe, and Jason Brown. 2016. “Assessment of Linear Emulators in Lightweight Bayesian Calibration of Dynamic Building Energy Models for Parameter Estimation and Performance Prediction.” Energy and Buildings 124: 194–202. doi: 10.1016/j.enbuild.2016.04.025
   Web of Science ®Google Scholar
• Li, Qi, Li Gu, Godfried Augenbroe, C. F. Jeff Wu, and Jason Brown. 2015. “Calibration of Dynamic Building Energy Models with Multiple Responses using Bayesian Inference and Linear Regression Models.” Energy Procedia 78: 979–984. doi: 10.1016/j.egypro.2015.11.037
   Google Scholar
• Li, Qi, Li Gu, G. A. Augenbroe, Jeff Wu, and Jason Brown. 2015. “A Generic Approach to Calibrate Building Energy Models Under Uncertainty Using Bayesian Inference.” 14th conference of International Building Performance Simulation Association, Hyderabad, India.
   Google Scholar
• Lim, Hyunwoo, and Zhiqiang John Zhai. 2017. “Review on Stochastic Modeling Methods for Building Stock Energy Prediction.” Building Simulation 10 (5): 607–624. doi: 10.1007/s12273-017-0383-y
   Web of Science ®Google Scholar
• Lopes, Hedibert F., and Justin L. Tobias. 2011. “Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis.” Annual Review of Economics 3 (1): 107–131. doi: 10.1146/annurev-economics-111809-125134
   Google Scholar
• Manfren, Massimiliano, Niccolò Aste, and Reza Moshksar. 2013. “Calibration and Uncertainty Analysis for Computer Models – A Meta-Model based Approach for Integrated Building Energy Simulation.” Applied Energy 103 (0): 627–641. doi: 10.1016/j.apenergy.2012.10.031
   Google Scholar
• Menberg, Kathrin, Yeonsook Heo, and Ruchi Choudhary. 2016. “Sensitivity Analysis Methods for Building Energy Models: Comparing Computational Costs and Extractable Information.” Energy and Buildings 133: 433–445. doi: 10.1016/j.enbuild.2016.10.005
   Web of Science ®Google Scholar
• Menberg, K., Y. Heo, and R. Choudhary. 2017. “Efficiency and Reliability of Bayesian Calibration of Energy Supply System Models.” In Building Simulation 2017, 1–10. San Fransisco: Conference Proceedings.
   Google Scholar
• Mihai, Andreea, and Radu Zmeureanu. 2017. “Bottom-up Evidence-based Calibration of the HVAC Air-side Loop of a Building Energy Model.” Journal of Building Performance Simulation 10: 105–123. doi: 10.1080/19401493.2016.1152302
   Web of Science ®Google Scholar
• Morris, Max D. 1991. “Factorial Sampling Plans for Preliminary Computational Experiments.” Technometrics 33 (2): 161–174. doi: 10.1080/00401706.1991.10484804
   Web of Science ®Google Scholar
• Muehleisen, R. T., and Joshua Bergerson. 2016. “Bayesian Calibration – What, Why and How.” International High Performance Buildings Conference. Purdue University.
   Google Scholar
• Mustafaraj, Giorgio, Dashamir Marini, Andrea Costa, and Marcus Keane. 2014. “Model Calibration for Building Energy Efficiency Simulation.” Applied Energy 130 (0): 72–85. doi: 10.1016/j.apenergy.2014.05.019
   Google Scholar
• Neal, Radford M. 2011. “MCMC Using Hamiltonian Dynamics.” Handbook of Markov Chain Monte Carlo 2: 113–162.
   Google Scholar
• Neto, Alberto Hernandez, and Flávio Augusto Sanzovo Fiorelli. 2008. “Comparison between Detailed Model Simulation and Artificial Neural Network for Forecasting Building Energy Consumption.” Energy and Buildings 40 (12): 2169–2176. doi: 10.1016/j.enbuild.2008.06.013
   Web of Science ®Google Scholar
• Niemelä, Tuomo, Mika Vuolle, Risto Kosonen, Juha Jokisalo, Waltteri Salmi, and Markus Nisula. 2016. “Dynamic Simulation Methods of Heat Pump Systems as a Part of Dynamic Energy Simulation of Buildings.” Paper presented at the Proceedings of BSO2016: 3th Conference of International Building Performance Simulation Association England, Newcastle, England.
   Google Scholar
• Omlin, Martin, and Peter Reichert. 1999. “A Comparison of Techniques for the Estimation of Model Prediction Uncertainty.” Ecological Modelling 115 (1): 45–59. doi: 10.1016/S0304-3800(98)00174-4
   Web of Science ®Google Scholar
• Reddy, T Agami, Itzhak Maor, S. Jian, and C. Panjapornporn. 2006. “Procedures for Reconciling Computer-Calculated Results with Measured Energy Data.” ASHRAE Research Project 1051-RP: 1–60.
   Google Scholar
• Roy, Christopher J., and William L. Oberkampf. 2011. “A Comprehensive Framework for Verification, Validation, and Uncertainty Quantification in Scientific Computing.” Computer Methods in Applied Mechanics and Engineering 200 (25–28): 2131–2144. doi:10.1016/j.cma.2011.03.016.
   Web of Science ®Google Scholar
• Silva, Arthur Santos, and Enedir Ghisi. 2014. “Uncertainty Analysis of the Computer Model in Building Performance Simulation.” Energy and Buildings 76: 258–269. doi: 10.1016/j.enbuild.2014.02.070.
   Web of Science ®Google Scholar
• Sun, Kaiyu, Tianzhen Hong, Sarah C. Taylor-Lange, and Mary Ann Piette. 2016. “A Pattern-Based Automated Approach to Building Energy Model Calibration.” Applied Energy 165: 214–224. doi: 10.1016/j.apenergy.2015.12.026.
   Web of Science ®Google Scholar
• Tian, Wei. 2013. “A Review of Sensitivity Analysis Methods in Building Energy Analysis.” Renewable and Sustainable Energy Reviews 20 (0): 411–419. doi:10.1016/j.rser.2012.12.014.
   Google Scholar
• Tian, Wei, Song Yang, Zhanyong Li, Shen Wei, Wei Pan, and Yunliang Liu. 2016. “Identifying Informative Energy Data in Bayesian Calibration of Building Energy Models.” Energy and Buildings 119: 363–376. doi: 10.1016/j.enbuild.2016.03.042.
   Web of Science ®Google Scholar
• Wang, Shuchun, Wei Chen, and Kwok-Leung Tsui. 2009. “Bayesian Validation of Computer Models.” Technometrics 51: 439–451. doi: 10.1198/TECH.2009.07011
   Web of Science ®Google Scholar
• Williams, Christopher KI, and Carl Edward Rasmussen. 2006. “Gaussian Processes for Machine Learning.” The MIT Press 2 (3): 4.
   Google Scholar
• Yang, Zheng, and Burcin Becerik-Gerber. 2015. “A Model Calibration Framework for Simultaneous Multi-level Building Energy Simulation.” Applied Energy 149: 415–431. doi: 10.1016/j.apenergy.2015.03.048
   Web of Science ®Google Scholar