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Review of developments in whole-building statistical energy consumption models for commercial buildings

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  • Fu, Hongxiang
  • Baltazar, Juan-Carlos
  • Claridge, David E.

Abstract

A significant portion of energy consumption occurs in buildings today. Accurate and easy-to-implement methods are needed to calculate building energy consumption for a wide range of applications. These areas have attracted research interest as early as the 1980's. Among a number of approaches for building energy analysis, the statistical methods have remained popular because they are simple to use and able to provide accurate prediction of building energy consumption. As the availability and quality of building energy data continue to improve, the methodologies behind building energy calculation also evolved over time. Although relevant areas such as calibrated simulation and machine learning methods have had numerous recent literature reviews, the statistical methods have not been reviewed in depth. This work aims to fill this knowledge gap for whole-building energy consumption modelling. This work will discuss how the methodology developed through time and summarise the applications of this approach in various areas of building energy analysis. This work has identified that the statistical methods have rarely been applied to model the electric demand, power factor, or domestic water use. The use of an occupancy variable and novel model forms are also areas with limited literature.

Suggested Citation

  • Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    • Handle: RePEc:eee:rensus:v:147:y:2021:i:c:s1364032121005359
    DOI: 10.1016/j.rser.2021.111248
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    6. Li, Tao & Liu, Xiangyu & Li, Guannan & Wang, Xing & Ma, Jiangqiaoyu & Xu, Chengliang & Mao, Qianjun, 2024. "A systematic review and comprehensive analysis of building occupancy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    7. Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    8. Balali, Amirhossein & Yunusa-Kaltungo, Akilu & Edwards, Rodger, 2023. "A systematic review of passive energy consumption optimisation strategy selection for buildings through multiple criteria decision-making techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).

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