Multi-Level Decomposition and Interpretability-Enhanced Air Conditioning Load Forecasting Study

Xinting Yang, Ling Zhang, Hong Zhao, Wenhua Zhang, Chuan Long, Gang Wu, Junhao Zhao and Xiaodong Shen ()
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Xinting Yang: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Ling Zhang: State Grid Sichuan Electric Power Company, Chengdu 610041, China
Hong Zhao: State Grid Sichuan Electric Power Company, Chengdu 610041, China
Wenhua Zhang: State Grid Sichuan Electric Power Company, Chengdu 610041, China
Chuan Long: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Gang Wu: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Junhao Zhao: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xiaodong Shen: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2024, vol. 17, issue 23, 1-20

Abstract: This study seeks to improve the accuracy of air conditioning load forecasting to address the challenges of load management in power systems during high-temperature periods in the summer. Given the limitations of traditional forecasting models in capturing different frequency components and noise within complex load sequences, this paper proposes a multi-level decomposition forecasting model using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), variational mode decomposition (VMD), and long short-term memory (LSTM). First, CEEMDAN is used for the preliminary decomposition of the raw air-conditioning load series, with modal components aggregated by sample entropy to generate high-, medium-, and low-frequency subsequences. VMD then performs a secondary decomposition on the high-frequency subsequence to reduce its complexity, while LSTM is applied to each subsequence for prediction. The final prediction result of the air-conditioning load is obtained through reconstruction. To validate model performance, this paper uses air-conditioning load data from Nanchong City and Sichuan Province, for experimental analysis. Results show that the proposed method significantly outperforms the LSTM model without decomposition and other benchmark models in prediction accuracy, with the Root Mean Square Error (RMSE) reductions ranging from 40.26% to 74.18% and the Modified Mean Absolute Percentage Error (MMAPE) reductions from 37.75% to 73.41%. By employing the SHAP (Shapley additive explanations) method for both global and local interpretability, the model reveals the influence of key factors, such as historical load and temperature, on load forecasting. The decomposition and aggregation approach introduced in this paper substantially enhances forecasting accuracy, providing a scientific foundation for power system load management and dispatch.

Keywords: air conditioning; load forecasting; fully adaptive noise empirical mode decomposition; variational mode decomposition; long short-term memory network; sample entropy; SHAP interpretability (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: 2024
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