Work

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

Year: 2025

Type: article

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.

Source: Energies

DOI: 10.3390/en18225932

Authors: Zhiming Gao, Miao Wang, Cheng Chen, Xuan Zhou, Wanchun Sun, Junwei Yan

Institutions: Shunde Polytechnic, South China University of Technology

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137