A methodology for auto-calibrating urban building energy models using surrogate modeling techniques

References

• Aijazi, A. N., and L. R. Glicksman. 2016. Comparison of Regression Techniques for Surrogate Models of Building Energy Performance ASHRAE/IBPSA-USA Bldg Simulation Conf.
   Google Scholar
• ALGLIB Project. 2012. ALGLIB Numerical Analysis Library. http://www.alglib.net/.
   Google Scholar
• American Society of Heating, Refrigeration and Air Conditioning Engineers. 2002. Guideline 14-2002 for Measurement of Energy and Demand Savings, ASHRAE
   Google Scholar
• American Society of Heating, Refrigeration and Air Conditioning Engineers. 2011. Standard 140-2011-Standard Method of Test for the Evaluation of Building Energy Analysis Computer Programs. Atlanta, GA: ASHRAE.
   Google Scholar
• American Society of Heating, Refrigeration and Air Conditioning Engineers. 2014. Standard 189.1-2014 Standard for the Design of High-performance Green Buildings. Atlanta, GA: ASHRAE.
   Google Scholar
• Bourdic, L., and S. Salat. 2012. “Building Energy Models and Assessment Systems at the District and City Scales: A Review.” Building Research & Information 40: 518–526. doi: 10.1080/09613218.2012.690951
   Web of Science ®Google Scholar
• Bricca, G., J. Gimber, B. Martin, J. Brian, J. Rollings, R. Schwartz, and P. Smith. 2017. Energy Efficiency of Colgate University Buildings A Performance Based Approach to Sustainability.
   Google Scholar
• Brieman, L. 2001. “Random Forests.” Machine Learning 45: 5–32. doi: 10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Brown, K. 2012. “Setting Enhanced Performance Targets for a new University Campus: Benchmarks vs. Energy Standards as a Reference?” American Council for an Energy-Efficient Economy Summer Study Proceedings 4: 29–40.
   Google Scholar
• Brown, N. 2016. Multi-objective Optimization for the Conceptual Design of Structures. Cambridge, MA: Massachusetts Institute of Technology.
   Google Scholar
• Brown, N., J. Ochsendorf, C. Mueller, and J. Felipe. 2016. -“Early-stage Integration of Architectural and Structural Performance in a Parametric Multi-Objective Design Tool: Beyond their Limits.” 1103–1111.
   Google Scholar
• Caldas, L., and L. Norford. 2003. “Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems.” Journal of Solar Energy Engineering-transactions of The Asme 125: 10–1115. doi: 10.1115/1.1591803
   Web of Science ®Google Scholar
• Cerezo, C., J. Sokol, C. Reinhart, and A. Al-Mumin. 2015. “Three Methods for Characterizing Building Archetypes in Urban Energy Simulation. A Case Study in Kuwait City.” In Proceedings 14th International IBPSA Building Simulation Conference. Hyderabad: IBPSA.
   Google Scholar
• Chen, Y., T. Hong, and M. A. Piette. 2017. “Automatic Generation and Simulation of Urban Building Energy Models Based on City Datasets for City-Scale Building Retrofit Analysis.” Applied Energy 205: 323–335. doi: 10.1016/j.apenergy.2017.07.128
   Web of Science ®Google Scholar
• Chung, M. H., and E. K. Rhee. 2014. “Potential Opportunities for Energy Conservation in Existing Buildings on University Campus: A Field Survey in Korea.” Energy and Buildings 78: 176–182. doi: 10.1016/j.enbuild.2014.04.018
   Web of Science ®Google Scholar
• Coakley, D., P. Raftery, and M. Keane. 2014. “A Review of Methods to Match Building Energy Simulation Models to Measured Data.” Renewable and Sustainable Energy Reviews 37: 123–141. doi: 10.1016/j.rser.2014.05.007
   Web of Science ®Google Scholar
• Coccolo, S., J. Kaempf, and J. Scartezzini. 2015. “The EPFL Campus in Lausanne: New Energy Strategies for 2050.” Energy Procedia 78: 3174–3179. doi: 10.1016/j.egypro.2015.11.776
   Google Scholar
• Crawley, D., J. Hand, and L. Lawry. 1999. “Improving the Weather Information Available to Simulation Programs.” In: Proceedings of building simulation 1999, Kyoto.
   Google Scholar
• Cui, Y., Z. Geng, Q. Zhu, and Y. Han. 2017. “Review Multi-Objective Optimization Methods and Application in Energy Saving.” Energy 125: 681–704. doi: 10.1016/j.energy.2017.02.174
   Web of Science ®Google Scholar
• Davila, C., C. F. Reinhart, and J. L. Bemis. 2016. “Modeling Boston: A Workflow for the Efficient Generation and Maintenance of Urban Building Energy Models From Existing Geospatial Datasets.” Energy 117: 237–50. doi: 10.1016/j.energy.2016.10.057
   Web of Science ®Google Scholar
• Dogan, T., and C. R. Reinhart. 2013. “Automated Conversion of Architectural Massing Models Into Thermal Shoebox Models.” In: Proceedings of building simulation 2013, Chambery.
   Google Scholar
• Escobedo, A., S. Briceño, H. Juárez, D. Castillo, M. Imaz, and C. Sheinbaum. 2014. “Energy Consumption and GHG Emission Scenarios of a University Campus in Mexico.” Energy for Sustainable Development 18: 49–57. doi: 10.1016/j.esd.2013.10.005
   Web of Science ®Google Scholar
• Fabrizio, E., and V. Monetti. 2015. “Methodologies and Advancements in the Calibration of Building Energy Models.” Energies 8: 2548–2574. doi: 10.3390/en8042548
   Web of Science ®Google Scholar
• Flöry, S. Goat. http://www.rechenraum.com/goat/download.html.
   Google Scholar
• Guan, J., N. Nord, and S. Chen. 2016. “Energy Planning of University Campus Building Complex: Energy Usage and Coincidental Analysis of Individual Buildings with a Case Study.” Energy and Buildings 124: 99–111. doi: 10.1016/j.enbuild.2016.04.051
   Web of Science ®Google Scholar
• Heidarinejad, M., J. G. Cedeño-Laurent, J. R. Wentz, N. M. Rekstad, J. D. Spengler, and J. Srebric. 2017a. “Actual Building Energy use Patterns and Their Implications for Predictive Modeling.” Energy Conversion and Management 144: 164–180. doi: 10.1016/j.enconman.2017.04.003
   Web of Science ®Google Scholar
• Heidarinejad, M., N. Mattise, M. Dahlhausen, K. Sharma, K. Benne, D. Macumber, L. Brackney, and J. Srebric. 2017b. “Demonstration of Reduced-order Urban Scale Building Energy Models.” Energy and Buildings 156: 17–28. doi: 10.1016/j.enbuild.2017.08.086
   Web of Science ®Google Scholar
• Heo, Y., R. Choudhary, and G. 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
• Howard, B., L. Parshall, J. Thompson, S. Hammer, J. Dickinson, and V. Modi. 2012. “Spatial Distribution of Urban Building Energy Consumption by end use.” Energy and Buildings 45: 141–151. doi: 10.1016/j.enbuild.2011.10.061
   Web of Science ®Google Scholar
• Lee, S. U., and D. E. Claridge. 2002. “Automatic Calibration of a Building Energy Simulation Model Using a Global Optimization Program.” In: Proceedings of the second international conference for enhanced building operations, Richardson, TX.
   Google Scholar
• Letkiewicz, D. 2010. The City College of New York (CCNY) Campus Energy Assessment (Final Report JDE #2841089999 Prepared for Dormitory Authority of the State of New York).
   Google Scholar
• Mueller, Caitlin. 2014. Computational Exploration of the Structural Design Space. Cambridge, MA: Massachusetts Institute of Technology.
   Google Scholar
• New, J., J. Sanyal, M. Bhandari, and S. Shrestha. 2012. “Autotune E+ Building Energy Models.” In: Proceedings of the fifth national conference of IBPSA-USA, Madison, WI.
   Google Scholar
• Office of Sustainability, Stanford University. 2015. Stanford University Energy and Climate Plan. Stanford, CA: Stanford University.
   Google Scholar
• Reddy, T. A., and I. Maor. 2006. “ Procedures for Reconciling Computer-Calculated Results with Measured Energy Data”, ASHRAE Research Project Report 1051-RP.
   Google Scholar
• Reddy, T. A., I. Maor, and C. Panjapornpon. 2007. “Calibrating Detailed Building Energy Simulation Programs with Measured Data – Part I: General Methodology.” HVAC & R Research 13: 221–41. doi: 10.1080/10789669.2007.10390952
   Web of Science ®Google Scholar
• Reinhart, C., and C. Cerezo. 2015. “Urban Building Energy Modeling: a Review of a Nascent Field.” Building and Environment 97: 196–202. doi: 10.1016/j.buildenv.2015.12.001
   Web of Science ®Google Scholar
• Reinhart, C. R., T. Dogan, J. A. Jakubiec, T. Rakha, and A. Sang. 2013. “UMI – An Urban Simulation Environment for Building Energy use, Daylighting and Walkability.” In: Proceedings of building simulation 2013, Chambery.
   Google Scholar
• Robert McNeel and Associates, Grasshopper: Algorithmic Modeling for Rhino. 2015. http://www.grasshopper3d.com/.
   Google Scholar
• Robert McNeel and Associates, Rhinoceros 3D. 2015. https://www.rhino3d.com/.
   Google Scholar
• Robertson, J., B. Polly, and J. Collis. 2013. Evaluation of Automated Model Calibration Techniques for Residential Building Energy Simulation. NREL Technical Report NREL/TP-5500-60127.
   Google Scholar
• Robinson, D., F. Haldi, and J. Kampf. 2009. “CitySim: Comprehensive Micro-Simulation Resource Flows for Sustainable Urban Planning.” In: Proceedings of the eleventh international IBPSA conference, Glasgow, 1083–1090.
   Google Scholar
• Sokol, J., C. C. Davila, and C. F. Reinhart. 2017. “Validation of a Bayesian-based Method for Defining Residential Archetypes in Urban Building Energy Models.” Energy and Buildings 134: 11–24. doi: 10.1016/j.enbuild.2016.10.050
   Web of Science ®Google Scholar
• Sun, K., T. Hong, S. C. Taylor-Lange, and M. A. Piette. 2016. “A Pattern-Based Automated Approach to Building Energy Model Calibration.” Applied Energy 165: 214–24. doi: 10.1016/j.apenergy.2015.12.026
   Web of Science ®Google Scholar
• Tsanas, A., and A. Xifara. 2012. “Accurate Quantitative Estimation of Energy Performance of Residential Buildings Using Statistical Machine Learning Tools.” Energy and Buildings 49: 560–567. doi: 10.1016/j.enbuild.2012.03.003
   Web of Science ®Google Scholar
• Tseranidis, S., N. Brown, and C. Mueller. 2016. “Data-driven Approximation Algorithms for Rapid Performance Evaluation and Optimization of Civil Structures.” Automation in Construction. 02-2002. doi:10.1016/j.autcon.2016.02.002.
   Web of Science ®Google Scholar
• U.S. 2015. “ Department of Energy, EnergyPlus Energy Simulation Software.” http://apps1.eere.energy.gov/buildings/energyplus/.
   Google Scholar
• US-DOE EPW Weather Data. http://apps1.eere.energy.gov/buildings/energyplus.
   Google Scholar
• Vásquez, L., A. Iriarte, M. Almeida, and P. Villalobos. 2015. “Evaluation of Greenhouse gas Emissions and Proposals for Their Reduction at a University Campus in Chile.” Journal of Cleaner Production 108 Part A, 924–930. doi: 10.1016/j.jclepro.2015.06.073
   Web of Science ®Google Scholar
• Yang, Z., and B. 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