Research on Monthly Energy Consumption Intensity Prediction and Climate Correlation of Public Institutions Based on Machine Learning

Zhiming Gao, Miao Wang, Cheng Chen, Xuan Zhou (), Wanchun Sun and Junwei Yan
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Zhiming Gao: School of Energy and Automotive Engineering, Shunde Polytechnic University, Foshan 528300, China
Miao Wang: School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Cheng Chen: School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Xuan Zhou: School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
Wanchun Sun: School of Energy and Automotive Engineering, Shunde Polytechnic University, Foshan 528300, China
Junwei Yan: School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China

Energies, 2025, vol. 18, issue 22, 1-23

Abstract: Energy consumption forecasting offers a foundation for governmental agencies to establish energy consumption benchmarks for public institutions. Meanwhile, correlation analysis of institutional energy use provides clear guidance for building energy-efficient retrofits. This study employed five machine learning models to train and predict monthly energy consumption intensity data from 2020 to 2022 for three types of public institutions in China’s eastern coastal regions. A novel ensemble model was proposed and applied for energy consumption prediction. Additionally, the SHAP model was utilized to analyze the correlation between influencing factors and energy consumption data. Finally, the relationship between climatic factors and monthly energy consumption intensity was investigated. Results indicate that the ensemble model achieves higher predictive accuracy compared to other models, with regression metrics on the training set generally exceeding 0.9. Although XGBoost also demonstrated strong performance, it was less stable than the ensemble model. Energy intensity across different building types exhibited strong correlations with the number of energy users, floor area, electricity use, and water consumption. Linear analysis of temperature and energy consumption intensity revealed a directional linear relationship between the two for both medical and administrative buildings.

Keywords: public institutions; energy consumption prediction; ensemble model; SHAP model; climatic factors (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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