Concomitant with the expeditious growth of the construction industry, the challenge of building energy consumption has become increasingly pronounced. A multitude of factors influence the energy consumption of building operations, thereby underscoring the paramount importance of monitoring and predicting such consumption. The advent of big data has engendered a diversification in the methodologies employed to predict building energy consumption. Against the backdrop of factors influencing building operation energy consumption, we reviewed the advancements in research pertaining to the supervision and prediction of building energy consumption, deliberated on more energy-efficient and low-carbon strategies for buildings within the dual-carbon context, and synthesized the relevant research progress across four dimensions: The contemporary state of building energy consumption supervision, the determinants of building operation energy consumption, and the prediction and optimization of building energy consumption. Building upon the investigation of supervision and determinants of building energy consumption, three predictive methodologies were examined: (ⅰ) Physical methods, (ⅱ) data-driven methods, and (ⅲ) mixed methods. An analysis of the accuracy of these three predictive methodologies revealed that the mixed methods exhibited superior precision in the actual prediction of building energy consumption. Furthermore, predicated on this foundation and the identified determinants, we also explored research on the optimization of energy consumption prediction. Through an in-depth examination of building energy consumption prediction, we distilled the methodologies pertinent to the accurate forecasting of building energy consumption, thereby offering insights and guidance for the pursuit of building energy conservation and emission reduction.
Citation: Zhongjiao Ma, Zichun Yan, Mingfei He, Haikuan Zhao, Jialin Song. A review of the influencing factors of building energy consumption and the prediction and optimization of energy consumption[J]. AIMS Energy, 2025, 13(1): 35-85. doi: 10.3934/energy.2025003
Concomitant with the expeditious growth of the construction industry, the challenge of building energy consumption has become increasingly pronounced. A multitude of factors influence the energy consumption of building operations, thereby underscoring the paramount importance of monitoring and predicting such consumption. The advent of big data has engendered a diversification in the methodologies employed to predict building energy consumption. Against the backdrop of factors influencing building operation energy consumption, we reviewed the advancements in research pertaining to the supervision and prediction of building energy consumption, deliberated on more energy-efficient and low-carbon strategies for buildings within the dual-carbon context, and synthesized the relevant research progress across four dimensions: The contemporary state of building energy consumption supervision, the determinants of building operation energy consumption, and the prediction and optimization of building energy consumption. Building upon the investigation of supervision and determinants of building energy consumption, three predictive methodologies were examined: (ⅰ) Physical methods, (ⅱ) data-driven methods, and (ⅲ) mixed methods. An analysis of the accuracy of these three predictive methodologies revealed that the mixed methods exhibited superior precision in the actual prediction of building energy consumption. Furthermore, predicated on this foundation and the identified determinants, we also explored research on the optimization of energy consumption prediction. Through an in-depth examination of building energy consumption prediction, we distilled the methodologies pertinent to the accurate forecasting of building energy consumption, thereby offering insights and guidance for the pursuit of building energy conservation and emission reduction.
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Figures(11) / Tables(6)
Zhongjiao Ma, Zichun Yan, Mingfei He, Haikuan Zhao, Jialin Song. A review of the influencing factors of building energy consumption and the prediction and optimization of energy consumption[J]. AIMS Energy, 2025, 13(1): 35-85. doi: 10.3934/energy.2025003
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