Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD

Xiaoyu Liu (), Jiangfeng Song, Hai Tao (), Peng Wang, Haihua Mo and Wenjie Du
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Xiaoyu Liu: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
Jiangfeng Song: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
Hai Tao: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
Peng Wang: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
Haihua Mo: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
Wenjie Du: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China

Energies, 2025, vol. 18, issue 11, 1-29

Abstract: Accurate long-term power load forecasting in the grid is crucial for supply–demand balance analysis in new power systems. It helps to identify potential power market risks and uncertainties in advance, thereby enhancing the stability and efficiency of power systems. Given the temporal and nonlinear features of power load, this paper proposes a hybrid load-forecasting model using attention mechanisms, CNN, and BiLSTM. Historical load data are processed via CEEMDAN, K-means clustering, and VMD for significant regularity and uncertainty feature extraction. The CNN layer extracts features from climate and date inputs, while BiLSTM captures short- and long-term dependencies from both forward and backward directions. Attention mechanisms enhance key information. This approach is applied for seasonal load forecasting. Several comparative experiments show the proposed model’s high accuracy, with MAPE values of 1.41%, 1.25%, 1.08% and 1.67% for the four seasons. It outperforms other methods, with improvements of 0.25–2.53 GWh 2 in MSE, 0.15–0.1 GWh in RMSE, 0.1–0.74 GWh in MAE and 0.22–1.40% in MAPE. Furthermore, the effectiveness of the data processing method and the impact of training data volume on forecasting accuracy are analyzed. The results indicate that decomposing and clustering historical load data, along with large-scale data training, can both boost forecasting accuracy.

Keywords: long-term load forecasting; CNN-BiLSTM; attention mechanism; CEEMDAN; K-means clustering; VMD (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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