Development of a metamodelling framework for building energy models with application to fifth-generation district heating and cooling networks

References

• Aijazi, Arfa N., and Leon R. Glicksman. 2016. “Comparison of Regression Techniques for Surrogate Models of Building Energy Performance.” In ASHRAE and IBPSA-USA SimBuild 2016 Building Performance Modeling Conference, 327–334.
   Google Scholar
• Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32.
   Web of Science ®Google Scholar
• Buffa, Simone, Marco Cozzini, Matteo D'antoni, Marco Baratieri, and Roberto Fedrizzi. 2019. “5th Generation District Heating and Cooling Systems: A Review of Existing Cases in Europe.” Renewable and Sustainable Energy Reviews 104: 504–522.
   Web of Science ®Google Scholar
• Burr, Andrew. 2012. “Energy Rating and Disclosure in the United States and Europe.” Technical Report. Institute for Market Transformation.
   Google Scholar
• Caucheteux, Antoine, Antoine Gautier, and Rofaïda Lahrech. 2016. “A Meta Model-Based Methodology for an Energy Savings Uncertainty Assessment of Building Retrofitting.” International Journal of Metrology and Quality Engineering 7 (4): 402.
   Google Scholar
• Cho, Honghyun, and Jong Min Choi. 2014. “The Quantitative Evaluation of Design Parameter's Effects on a Ground Source Heat Pump System.” Renewable Energy 65: 2–6.
   Web of Science ®Google Scholar
• DOE. 2018. “EnergyPlus Energy Simulation Software.”
   Google Scholar
• DOE (Department of Energy). 2018. “Commercial Prototype Building Models.”
   Google Scholar
• Dong, Bing, Cheng Cao, and Siew Eang Lee. 2005. “Applying Support Vector Machines to Predict Building Energy Consumption in Tropical Region.” Energy and Buildings 37 (5): 545–553.
   Web of Science ®Google Scholar
• Eisenhower, Bryan, Zheng O'Neill, Satish Narayanan, Vladimir A. Fonoberov, and Igor Mezić. 2012. “A Methodology for Meta-model Based Optimization in Building Energy Models.” Energy and Buildings 47: 292–301.
   Web of Science ®Google Scholar
• F-Chart Software. “EES: Engineering Equation Solver.”
   Google Scholar
• Feng, Guohui, Dandan Chi, Xiaolong Xu, Baoyue Dou, Yixin Sun, and Yao Fu. 2017. “Study on the Influence of Window-Wall Ratio on the Energy Consumption of Nearly Zero Energy Buildings.” Procedia Engineering 205: 730–737.
   Google Scholar
• Foucquier, Aurélie, Sylvain Robert, Frédéric Suard, Louis Stéphan, and Arnaud Jay. 2013. “State of the Art in Building Modelling and Energy Performances Prediction: A Review.” Renewable and Sustainable Energy Reviews 23: 272–288.
   Web of Science ®Google Scholar
• Goel, Supriya, Nora Wang, Henry Horsey, and Nicholas Long. 2016. “Building Efficiency Evaluation and Uncertainty Analysis with DOE's Asset Score Preview.” In ASHRAE and IBPSA-USA SimBuild 2016 Building, 173–180. Salt Lake City, UT.
   Google Scholar
• Guglielmetti, Robert, Daniel Macumber, and Nicholas L. Long. 2011. “OpenStudio: An Open Source Integrated Analysis Platform.” In Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association, 1–9.
   Google Scholar
• Heidarinejad, Mohammad, Jose G. Cedeño-Laurent, Joshua R. Wentz, Nicholas M. Rekstad, John D. Spengler, and Jelena Srebric. 2017. “Actual Building Energy Use Patterns and Their Implications for Predictive Modeling.” Energy Conversion and Management 144: 164–180.
   Web of Science ®Google Scholar
• Hong, Tianzhen, Fred Buhl, and Phil Haves. 2009. EnergyPlus Run Time Analysis. Technical Report, April. Berkeley, CA: Lawrence Berkeley National Laboratory (LBNL).
   Google Scholar
• Huang, D.-S., K Li, G. W. Irwin, Ruiming Liu, Erqi Liu, Jie Yang, Ming Li, and Fanglin Wang. 2006. “Optimizing the Hyper-parameters for SVM by Combining Evolution Strategies with a Grid Search.” Technical Report. Institute of Image Processing and Pattern Recognition.
   Google Scholar
• Hygh, Janelle S., Joseph F. DeCarolis, David B. Hill, and S. Ranji Ranjithan. 2012. “Multivariate Regression As An Energy Assessment Tool in Early Building Design.” Building and Environment 57: 165–175.
   Web of Science ®Google Scholar
• IBPSA-USA. “Building Energy Software Tools Directory.”
   Google Scholar
• IES. “VE for Architects and Engineers.”
   Google Scholar
• Jaffal, Issa, Christian Inard, and Christian Ghiaus. 2009. “Fast Method to Predict Building Heating Demand Based on the Design of Experiments.” Energy and Buildings 41 (6): 669–677.
   Web of Science ®Google Scholar
• Kalogirou, Soteris A., and Milorad Bojic. 2000. “Artificial Neural Networks for the Prediction of the Energy Consumption of a Passive Solar Building.” Energy 25 (5): 479–491.
   Web of Science ®Google Scholar
• Kavgic, M., A. Mavrogianni, D. Mumovic, A. Summerfield, Z. Stevanovic, and M. Djurovic-Petrovic. 2010. “A Review of Bottom-up Building Stock Models for Energy Consumption in the Residential Sector.” Building and Environment 45 (7): 1683–1697.
   Web of Science ®Google Scholar
• Kaynakli, Omer. 2012. “A Review of the Economical and Optimum Thermal Insulation Thickness for Building Applications.” Renewable and Sustainable Energy Reviews 16 (1): 415–425.
   Web of Science ®Google Scholar
• Klein, Sanford A., W. A. Beckman, and John A. Duffie. 1976. “TRNSYS: A Transient Simulation Program.” ASHRAE Transactions 82: 623.
   Google Scholar
• Krarti, M., D. Cohen, A. Dhar, T. A. Reddy, and D. E. Claridge. 1998. “Estimation of Energy Savings for Building Retrofits Using Neural Networks.” Journal of Solar Energy Engineering, Transactions of the ASME 120 (3): 217–223.
   Web of Science ®Google Scholar
• Landsberg, Dennis R., John A. Shonder, Kimberly A. Barker, Jeff S. Haberl, Scott A. Judson, David A. Jump, and William E. Koran, et al. 2014. ASHRAE Guideline 14-2014: Measurement of Energy, Demand, and Water Savings. Technical Report. Atlanta, GA: ASHRAE.
   Google Scholar
• Li, Qiong, Qinglin Meng, Jiejin Cai, Hiroshi Yoshino, and Akashi Mochida. 2009. “Predicting Hourly Cooling Load in the Building: A Comparison of Support Vector Machine and Different Artificial Neural Networks.” Energy Conversion and Management 50: 90–96.
   Web of Science ®Google Scholar
• Liang, Qing, Jian Fei Liu, Jing Liu, and Gang Xu. 2011. “Effect of External Wall Insulation on Building Energy Consumption.” In Frontiers of Green Building, Materials and Civil Engineering, Vol. 71 of Applied Mechanics and Materials, 156–159. Trans Tech.
   Google Scholar
• Melo, A. P., D. Cóstola, R. Lamberts, and J. L. M Hensen. 2014. “Development of Surrogate Models Using Artificial Neural Network for Building Shell Energy Labelling.” Energy Policy 69: 457–466.
   Web of Science ®Google Scholar
• Melo, A. P., R. S. Versage, G. Sawaya, and R. Lamberts. 2016. “A Novel Surrogate Model to Support Building Energy Labelling System: A New Approach to Assess Cooling Energy Demand in Commercial Buildings.” Energy and Buildings 131: 233–247.
   Web of Science ®Google Scholar
• Modelica Association. 2000. “Modelica.”
   Google Scholar
• NREL. 2019a. “OpenStudio Analysis Framework Version 2.8.1.”
   Google Scholar
• NREL. 2019b. “Parametric Analysis Tool Version 2.8.1.”
   Google Scholar
• Ostergard, Torben, Rasmus Lund Jensen, and Steffen Enersen Maagaard. 2018. “A Comparison of Six Metamodeling Techniques Applied to Building Performance Simulations.” Applied Energy 211: 89–103.
   Web of Science ®Google Scholar
• Polly, Benjamin, Chuck Kutscher, Daniel Macumber, and Marjorie Schott. 2016. “From Zero Energy Buildings to Zero Energy Districts.” In 2016 ACEEE Summer Study on Energy Efficiency in Buildings, Asilomar, CA, 1–16.
   Google Scholar
• Roth, Amir. 2016. “New OpenStudio-Standards Gem Delivers One Two Punch.”
   Google Scholar
• Roth, Amir, David Goldwasser, and Andrew Parker. 2016. “There's a Measure for That!.” Energy and Buildings 117: 321–331.
   Web of Science ®Google Scholar
• Ruch, David, and David E. Claridge. 1992. “A Four-Parameter Change-Point Model for Predicting Energy Consumption in Commercial Buildings.” Journal of Solar Energy Engineering 114 (2): 77.
   Web of Science ®Google Scholar
• Shi, Zixiao, and William O'Brien. 2018. “Building Energy Model Reduction Using Model-Cluster-Reduce Pipeline.” Journal of Building Performance Simulation 11 (5): 553–567. doi:10.1080/19401493.2017.1410572.
   Google Scholar
• Smola, Alex J., and Bernhard Scholkopf. 2004. “A Tutorial on Support Vector Regression.” Technical Report. Australian National University and Max-Planck-Institut fur biologische Kybernetik.
   Google Scholar
• Sonderegger, R. C. 1978. “Dynamic Models of House Heating Based on Equivalent Thermal Parameters.” PhD thesis, Princeton University, NJ.
   Google Scholar
• Su, Xing, and Xu Zhang. 2010. “Environmental Performance Optimization of Window-Wall Ratio for Different Window Type in Hot Summer and Cold Winter Zone in China Based on Life Cycle Assessment.” Energy and Buildings 42: 198–202.
   Web of Science ®Google Scholar
• Van Gelder, Liesje, Payel Das, Hans Janssen, and Staf Roels. 2014. “Comparative Study of Metamodelling Techniques in Building Energy Simulation: Guidelines for Practitioners.” Simulation Modelling Practice and Theory 49: 245–257.
   Web of Science ®Google Scholar
• Veloso, Ana Carolina O., Raphael N. Oliveira, Ricardo N. N. Koury, and Roberta V. G. Souza. 2018. “Energy Efficiency Labeling: Study About the Influence of Schedule Definitions.” Journal of Renewable and Sustainable Energy 10 (3).
   Web of Science ®Google Scholar
• von Rhein, Justus. 2018. “Modeling, Simulation, and Life-Cost Analysis of Fifth-Generation District Heating and Cooling Networks.”
   Google Scholar
• von Rhein, Justus, Gregor P. Henze, Nicholas Long, and Yangyang Fu. 2019. “Development of a Topology Analysis Tool for Fifth-Generation District Heating and Cooling Networks.” Energy Conversion and Management 196: 705–716.
   Web of Science ®Google Scholar
• Wetter, Michael, Wangda Zuo, Thierry S. Nouidui, and Xiufeng Pang. 2014. “Modelica Buildings Library.” Journal of Building Performance Simulation 7 (4): 253–270.
   Web of Science ®Google Scholar
• Winkelmann, F. C., B. E. Birdsall, W. F. Buhl, K. L. Ellington, and A. E. Erdem. 1993. “DOE-2 Supplement: Version 2.1E.”
   Google Scholar