The Prediction of Civil Building Energy Consumption Using a Hybrid Model Combining Wavelet Transform with SVR and ELM: A Case Study of Jiangsu Province

Xiangxu Chen, Jinjin Mu, Zihan Shang and Xinnan Gao ()
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Xiangxu Chen: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Jinjin Mu: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Zihan Shang: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China
Xinnan Gao: College of Information Management, Nanjing Agricultural University, Nanjing 211800, China

Mathematics, 2025, vol. 13, issue 14, 1-20

Abstract: As a pivotal economic province in China, Jiangsu’s efforts in civil building energy conservation are critical to achieving the national “dual carbon” goals. This paper proposes a hybrid model that integrates wavelet transform, support vector regression (SVR), and extreme learning machine (ELM) to predict the civil building energy consumption of Jiangsu Province. Based on data from statistical yearbooks, the historical energy consumption of civil buildings is calculated. Through a grey relational analysis (GRA), the key factors influencing the civil building energy consumption are identified. The wavelet transform technique is then applied to decompose the energy consumption data into a trend component and a fluctuation component. The SVR model predicts the trend component, while the ELM model captures the fluctuation patterns. The final prediction results are generated by combining these two predictions. The results demonstrate that the hybrid model achieves superior performance with a Mean Absolute Percentage Error (MAPE) of merely 1.37%, outperforming both individual prediction methods and alternative hybrid approaches. Furthermore, we develop three prospective scenarios to analyze civil building energy consumption trends from 2023 to 2030. The analysis reveals that the observed patterns align with the Environmental Kuznets Curve (EKC). These findings provide valuable insights for provincial governments in future policy-making and energy planning.

Keywords: energy consumption; civil building; wavelet transform; support vector regression; extreme learning machine (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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