Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation

Applied Energy - Tập 103 - Trang 627-641 - 2013
Massimiliano Manfren1, Niccolò Aste1, Reza Moshksar1
1Building Environment Science & Technology Department, Politecnico di Milano, Via Bonardi 3, 20133 Milano, Italy

Tài liệu tham khảo

Crawley, 2008, Contrasting the capabilities of building energy performance simulation programs, Build Environ, 43, 661, 10.1016/j.buildenv.2006.10.027 Eisenhower, 2012, Uncertainty and sensitivity decomposition of building energy models, J Build Perform Simulat, 5, 171, 10.1080/19401493.2010.549964 Al-Homoud, 2004, The effectiveness of thermal insulation in different types of buildings in hot climates, J Therm Envelope Build Sci, 27, 235, 10.1177/1097196304038368 Al-Homoud, 2005, A systematic approach for the thermal design optimization of building envelopes, J Building Phys, 29, 95, 10.1177/1744259105056267 Diakaki, 2010, A multi-objective decision model for the improvement of energy efficiency in buildings, Energy, 35, 5483, 10.1016/j.energy.2010.05.012 Haberl, 1998, Procedures for calibrating hourly simulation models to measured building energy and environmental data, J Sol Energy Eng, 120, 193, 10.1115/1.2888069 Heiselberg, 2009, Application of sensitivity analysis in design of sustainable buildings, Renew Energy, 34, 2030, 10.1016/j.renene.2009.02.016 Jensen, 2009, A Bayesian network approach to the evaluation of building design and its consequences for employee performance and operational costs, Build Environ, 44, 456, 10.1016/j.buildenv.2008.04.008 Chantrelle, 2011, Development of a multicriteria tool for optimizing the renovation of buildings, Appl Energy, 88, 1386, 10.1016/j.apenergy.2010.10.002 Magnier, 2010, Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and artificial neural network, Build Environ, 45, 739, 10.1016/j.buildenv.2009.08.016 Ochoa, 2012, Considerations on design optimization criteria for windows providing low energy consumption and high visual comfort, Appl Energy, 95, 238, 10.1016/j.apenergy.2012.02.042 Chowdhury, 2008, Thermal-comfort analysis and simulation for various low-energy cooling-technologies applied to an office building in a subtropical climate, Appl Energy, 85, 449, 10.1016/j.apenergy.2007.10.001 Nikolaou, 2009, Virtual building dataset for energy and indoor thermal comfort benchmarking of office buildings in Greece, Energy Build, 41, 1409, 10.1016/j.enbuild.2009.08.011 Al-Homoud, 2009, Envelope thermal design optimization of buildings with intermittent occupancy, J Building Phys, 33, 65, 10.1177/1744259109102799 Breesch, 2010, Performance evaluation of passive cooling in office buildings based on uncertainty and sensitivity analysis, Sol Energy, 84, 1453, 10.1016/j.solener.2010.05.008 Georgescu C, Afshari A, Bornard G. Optimal adaptive predictive control and fault detection of residential building heating systems. In: Proceedings of the third IEEE conference on control applications, vol. 3; 1994. p. 1601–6 Karatasou, 2006, Modeling and predicting building’s energy use with artificial neural networks: methods and results, Energy Build, 38, 949, 10.1016/j.enbuild.2005.11.005 Li, 2009, Predicting hourly cooling load in the building: a comparison of support vector machine and different artificial neural networks, Energy Convers Manage, 50, 90, 10.1016/j.enconman.2008.08.033 Mahdavi, 2001, Simulation-based control of building systems operation, Build Environ, 36, 789, 10.1016/S0360-1323(00)00065-2 Krarti, 2000 Reddy TA. Automated fault detection and diagnosis for hvac&r systems: functional description and lessons learnt. In: 2008 Proceedings of the 2nd international conference on energy sustainability, ES 2008, vol. 1; 2009. p. 589–99. Costa A, Keane MM, Torrens JI, Corry E. Building operation and energy performance. Monitoring, analysis and optimisation toolkit. Appl Energy 2011. Chung, 2012, Building energy demand patterns for department stores in Korea, Appl Energy, 90, 241, 10.1016/j.apenergy.2011.05.008 Struck, 2009, On the application of uncertainty and sensitivity analysis with abstract building performance simulation tools, J Build Phys, 33, 5, 10.1177/1744259109103345 Yoon, 2003, Calibration procedure for energy performance simulation of a commercial building, J Sol Energy Eng, 125, 251, 10.1115/1.1564076 Wetter, 2005, BuildOpt—a new building energy simulation program that is built on smooth models, Build Environ, 40, 1085, 10.1016/j.buildenv.2004.10.003 Wetter, 2005, Building design optimization using a convergent pattern search algorithm with adaptive precision simulations, Energy Build, 37, 603, 10.1016/j.enbuild.2004.09.005 Westphal, 2005 Zhou, 2006, Decision analysis in energy and environmental modeling: an update, Energy, 31, 2604, 10.1016/j.energy.2005.10.023 Zhou, 2008, A survey of data envelopment analysis in energy and environmental studies, Eur J Oper Res, 189, 1, 10.1016/j.ejor.2007.04.042 Popescu, 2012, Impact of energy efficiency measures on the economic value of buildings, Appl Energy, 89, 454, 10.1016/j.apenergy.2011.08.015 Corgnati SP, Fabrizio E, Filippi M, Monetti V. Reference buildings for cost optimal analysis: method of definition and application. Appl Energy 2012. Wan, 2012, Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications, Appl Energy, 97, 274, 10.1016/j.apenergy.2011.11.048 Lam, 2010, Impact of climate change on commercial sector air conditioning energy consumption in subtropical Hong Kong, Appl Energy, 87, 2321, 10.1016/j.apenergy.2009.11.003 Wetter, 2004, A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization, Build Environ, 39, 989, 10.1016/j.buildenv.2004.01.022 Soratana, 2010, Increasing innovation in home energy efficiency: Monte Carlo simulation of potential improvements, Energy Build, 42, 828, 10.1016/j.enbuild.2009.12.003 Lam, 2008, Sensitivity analysis and energy conservation measures implications, Energy Convers Manage, 49, 3170, 10.1016/j.enconman.2008.05.022 Kaldate, 2006, Engineering parameter selection for design optimization during preliminary design, J Eng Des, 17, 291, 10.1080/09544820500274027 Torcellini, 2006 Soebarto, 2001, Multi-criteria assessment of building performance. Theory and implementation, Build Environ, 36, 681, 10.1016/S0360-1323(00)00068-8 Reddy, 2011 Zádor, 2006, Local and global uncertainty analysis of complex chemical kinetic systems, Reliability Engineering and System Safety, 91, 1232, 10.1016/j.ress.2005.11.020 Saltelli, 2006, Sensitivity analysis practices: strategies for model-based inference, Reliab Eng System Safety, 91, 1109, 10.1016/j.ress.2005.11.014 Saltelli, 2008 Eisenhower, 2012, A methodology for meta-model based optimization in building energy models, Energy Build, 47, 292, 10.1016/j.enbuild.2011.12.001 O’Sullivan, 2004, Improving building operation by tracking performance metrics throughout the building lifecycle (BLC), Energy Build, 36, 1075, 10.1016/j.enbuild.2004.03.003 Granderson, 2009 Perry, 2008, Bond graph based sensitivity and uncertainty analysis modelling for micro-scale multiphysics robust engineering design, J Franklin Inst, 345, 282, 10.1016/j.jfranklin.2007.10.002 Pisello, 2012, A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity, Appl Energy, 97, 419, 10.1016/j.apenergy.2011.12.094 Dhar, 1999, A Fourier series model to predict hourly heating and cooling energy use in commercial buildings with outdoor temperature as the only weather variable, J Sol Energy Eng, 121, 47, 10.1115/1.2888142 Haberl, 2003, Inverse modeling toolkit (1050RP): application and testing, ASHRAE Trans, 109, 435 Kissock, 1998, Ambient-temperature regression analysis for estimating retrofit savings in commercial buildings, J Sol Energy Eng, 120, 168, 10.1115/1.2888066 Duda, 2001 Schölkopf, 2002 Shawe-Taylor, 2004 Rasmussen, 2006 BakIr, 2007 Reddy, 1998, Uncertainty in baseline regression modeling and in determination of retrofit savings, J Sol Energy Eng, 120, 185, 10.1115/1.2888068 Kennedy, 2001, Bayesian calibration of computer models, J Roy Stat Soc: Ser B (Stat Meth), 63, 425, 10.1111/1467-9868.00294 Oakley, 2004, Probabilistic sensitivity analysis of complex models: a Bayesian approach, J Roy Stat Soc: Ser B (Stat Meth), 66, 751, 10.1111/j.1467-9868.2004.05304.x Higdon, 2008, Computer model calibration using high-dimensional output, J Am Stat Assoc, 103, 570, 10.1198/016214507000000888 Busby, 2009, Hierarchical adaptive experimental design for Gaussian process emulators, Reliability Engineering and System Safety, 94, 1183, 10.1016/j.ress.2008.07.007 Andradóttir, 2007 Olofsson, 2012, Modeling and simulation of the energy use in an occupied residential building in cold climate, Appl Energy, 91, 432, 10.1016/j.apenergy.2011.10.002 Woodward, 2011 Bolstad, 2010 Hastie, 2009 Witten, 2011 Brown, 2011, Kernel regression for real-time building energy analysis, Journal of Building Performance Simulation, 1 Henze, 1997, Development of a predictive optimal controller for thermal energy storage systems, HVAC&R Res, 3, 233, 10.1080/10789669.1997.10391376 Kohavi, 1997, Wrappers for feature subset selection, Artif Intell, 97, 273, 10.1016/S0004-3702(97)00043-X Blum, 1997, Selection of relevant features and examples in machine learning, Artif Intell, 97, 245, 10.1016/S0004-3702(97)00063-5 Powell, 2012 Reshef, 2011, Detecting novel associations in large data sets, Science, 334, 1518, 10.1126/science.1205438 Sever F, Kissock JK, Brown D, Mulqueen S. estimating industrial building energy savings using inverse simulation. ASHRAE2011-86073. Las Vegas; 2011. Meyn S, Surana A, Yiqing L, Oggianu SM, Narayanan S, Frewen TA. A sensor-utility-network method for estimation of occupancy in buildings. In: Decision and control, 2009 held jointly with the 2009 28th Chinese control conference CDC/CCC 2009 proceedings of the 48th IEEE conference on2009; 2009. p. 1494–500. Melfi R, Rosenblum B, Nordman B, Christensen K. Measuring building occupancy using existing network infrastructure. In: Green computing conference and workshops (IGCC), 2011 international; 2011. p. 1–8. Chenda L, Barooah P. A novel stochastic agent-based model of building occupancy. In: American control conference (ACC); 2011. p. 2095–100. Liao, 2011, Agent-based and graphical modelling of building occupancy, J Build Perform Simulat, 5, 5, 10.1080/19401493.2010.531143 Chenda L, Barooah P. An integrated approach to occupancy modeling and estimation in commercial buildings. In: American control conference (ACC); 2010. p. 3130–5. Heng-Tao W, Qing-Shan J, Chen S, Ruixi Y, Xiaohong G. Estimation of occupancy level in indoor environment based on heterogeneous information fusion. In: Decision and control (CDC), 2010 49th IEEE conference on2010; 2010. p. 5086–91. Roweis, 1999, A unifying review of linear Gaussian models, Neural Comput, 11, 305, 10.1162/089976699300016674 Antony, 2003 Flager, 2009, Multidisciplinary process integration and design optimization of a classroom building, J Inform Technol Constr, 14, 595 Ng, 2008, Response surface models for CFD predictions of air diffusion performance index in a displacement ventilated office, Energy Build, 40, 774, 10.1016/j.enbuild.2007.04.024