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Methods for benchmarking building energy consumption against its past or intended performance: An overview

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  1. Molina-Solana, Miguel & Ros, María & Ruiz, M. Dolores & Gómez-Romero, Juan & Martin-Bautista, M.J., 2017. "Data science for building energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 598-609.
  2. Seyed Azad Nabavi & Alireza Aslani & Martha A. Zaidan & Majid Zandi & Sahar Mohammadi & Naser Hossein Motlagh, 2020. "Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors," Energies, MDPI, vol. 13(19), pages 1-22, October.
  3. Khayatian, Fazel & Sarto, Luca & Dall'O', Giuliano, 2017. "Building energy retrofit index for policy making and decision support at regional and national scales," Applied Energy, Elsevier, vol. 206(C), pages 1062-1075.
  4. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
  5. Wang, Endong, 2015. "Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach," Applied Energy, Elsevier, vol. 146(C), pages 92-103.
  6. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
  7. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
  8. Shiyi Song & Hong Leng & Ran Guo, 2022. "Multi-Agent-Based Model for the Urban Macro-Level Impact Factors of Building Energy Consumption on Different Types of Land," Land, MDPI, vol. 11(11), pages 1-24, November.
  9. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
  10. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network," Applied Energy, Elsevier, vol. 306(PA).
  11. Kwok Wai Mui & Ling Tim Wong & Manoj Kumar Satheesan & Anjana Balachandran, 2021. "A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong," Energies, MDPI, vol. 14(16), pages 1-18, August.
  12. Moya, Diego & Torres, Roberto & Stegen, Sascha, 2016. "Analysis of the Ecuadorian energy audit practices: A review of energy efficiency promotion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 289-296.
  13. Kaivo-oja, Jari & Vehmas, Jarmo & Luukkanen, Jyrki, 2016. "Trend analysis of energy and climate policy environment: Comparative electricity production and consumption benchmark analyses of China, Euro area, European Union, and United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 464-474.
  14. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
  15. Robinson, Caleb & Dilkina, Bistra & Hubbs, Jeffrey & Zhang, Wenwen & Guhathakurta, Subhrajit & Brown, Marilyn A. & Pendyala, Ram M., 2017. "Machine learning approaches for estimating commercial building energy consumption," Applied Energy, Elsevier, vol. 208(C), pages 889-904.
  16. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
  17. Liu, Jiangyan & Wang, Jiangyu & Li, Guannan & Chen, Huanxin & Shen, Limei & Xing, Lu, 2017. "Evaluation of the energy performance of variable refrigerant flow systems using dynamic energy benchmarks based on data mining techniques," Applied Energy, Elsevier, vol. 208(C), pages 522-539.
  18. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
  19. Kočí, Jan & Maděra, Jiří & Černý, Robert, 2015. "A fast computational approach for the determination of thermal properties of hollow bricks in energy-related calculations," Energy, Elsevier, vol. 83(C), pages 749-755.
  20. Yan, Chengchu & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "A multi-level energy performance diagnosis method for energy information poor buildings," Energy, Elsevier, vol. 83(C), pages 189-203.
  21. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  22. Juaidi, Adel & AlFaris, Fadi & Montoya, Francisco G. & Manzano-Agugliaro, Francisco, 2016. "Energy benchmarking for shopping centers in Gulf Coast region," Energy Policy, Elsevier, vol. 91(C), pages 247-255.
  23. Wanrui Qu & Alexander Jordan & Bowen Yang & Yuxiang Chen, 2024. "In Situ Performance Analysis of Hybrid Fuel Heating System in a Net-Zero Ready House," Sustainability, MDPI, vol. 16(3), pages 1-29, January.
  24. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
  25. Tran, Duc-Hoc & Luong, Duc-Long & Chou, Jui-Sheng, 2020. "Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings," Energy, Elsevier, vol. 191(C).
  26. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
  27. Arjunan, Pandarasamy & Poolla, Kameshwar & Miller, Clayton, 2020. "EnergyStar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, Elsevier, vol. 276(C).
  28. Benedetti, Miriam & Bonfa', Francesca & Bertini, Ilaria & Introna, Vito & Ubertini, Stefano, 2018. "Explorative study on Compressed Air Systems’ energy efficiency in production and use: First steps towards the creation of a benchmarking system for large and energy-intensive industrial firms," Applied Energy, Elsevier, vol. 227(C), pages 436-448.
  29. Liu, Jiangyan & Chen, Huanxin & Liu, Jiahui & Li, Zhengfei & Huang, Ronggeng & Xing, Lu & Wang, Jiangyu & Li, Guannan, 2017. "An energy performance evaluation methodology for individual office building with dynamic energy benchmarks using limited information," Applied Energy, Elsevier, vol. 206(C), pages 193-205.
  30. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation," Energy, Elsevier, vol. 244(PB).
  31. Hong, Taehoon & Koo, Choongwan & Kim, Daeho & Lee, Minhyun & Kim, Jimin, 2015. "An estimation methodology for the dynamic operational rating of a new residential building using the advanced case-based reasoning and stochastic approaches," Applied Energy, Elsevier, vol. 150(C), pages 308-322.
  32. Claudio Caromba & Corné Schutte & Jean van Laar, 2023. "Application of Clustering Techniques for Improved Energy Benchmarking on Deep-Level Mines," Energies, MDPI, vol. 16(19), pages 1-18, September.
  33. Longo, Stefano & d’Antoni, Benedetto Mirko & Bongards, Michael & Chaparro, Antonio & Cronrath, Andreas & Fatone, Francesco & Lema, Juan M. & Mauricio-Iglesias, Miguel & Soares, Ana & Hospido, Almudena, 2016. "Monitoring and diagnosis of energy consumption in wastewater treatment plants. A state of the art and proposals for improvement," Applied Energy, Elsevier, vol. 179(C), pages 1251-1268.
  34. Salah Bouktif & Ali Ouni & Sanja Lazarova-Molnar, 2022. "Towards a Rigorous Consideration of Occupant Behaviours of Residential Households for Effective Electrical Energy Savings: An Overview," Energies, MDPI, vol. 15(5), pages 1-30, February.
  35. Attia, Shady & Shadmanfar, Niloufar & Ricci, Federico, 2020. "Developing two benchmark models for nearly zero energy schools," Applied Energy, Elsevier, vol. 263(C).
  36. Capizzi, Giacomo & Sciuto, Grazia Lo & Cammarata, Giuliano & Cammarata, Massimiliano, 2017. "Thermal transients simulations of a building by a dynamic model based on thermal-electrical analogy: Evaluation and implementation issue," Applied Energy, Elsevier, vol. 199(C), pages 323-334.
  37. Wang, Ning & Wen, Zongguo & Liu, Mingqi & Guo, Jie, 2016. "Constructing an energy efficiency benchmarking system for coal production," Applied Energy, Elsevier, vol. 169(C), pages 301-308.
  38. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  39. Cai, Wei & Liu, Fei & Zhou, XiaoNa & Xie, Jun, 2016. "Fine energy consumption allowance of workpieces in the mechanical manufacturing industry," Energy, Elsevier, vol. 114(C), pages 623-633.
  40. Amir Shahcheraghian & Hatef Madani & Adrian Ilinca, 2024. "From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices," Energies, MDPI, vol. 17(2), pages 1-45, January.
  41. Shiyi Song & Hong Leng & Han Xu & Ran Guo & Yan Zhao, 2020. "Impact of Urban Morphology and Climate on Heating Energy Consumption of Buildings in Severe Cold Regions," IJERPH, MDPI, vol. 17(22), pages 1-24, November.
  42. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
  43. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
  44. Na, Wei & Wang, Mingming, 2022. "A Bayesian approach with urban-scale energy model to calibrate building energy consumption for space heating: A case study of application in Beijing," Energy, Elsevier, vol. 247(C).
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