A Novel Hybrid Model Combining LMD, MSCA, and SCINet for Electricity Forecasting

Abstract

To address the challenges of modeling complex nonlinear features and multi-scale temporal patterns in electricity forecasting, this paper proposes a SCINet model optimized with Local Mean Decomposition (LMD) and Multi-Scale Channel Attention (MSCA). The model first applies LMD to decompose the original electricity consumption sequence into multiple intrinsic mode functions, effectively capturing trends and fluctuations at different frequencies. Building on the SCINet framework, the MSCA module is introduced to enhance the model’s ability to focus on critical features through multi-scale feature extraction and inter-channel correlation modeling, enabling it to better capture variations and dependencies across different time scales. Experimental results show that in single-step, short-term prediction, the proposed LMD-MSCA-SCINet model achieves prediction results of MAE = 0.1539, MSE = 0.0901 and R2 = 0.9003, which are 63.4% and 72.1% lower than the Informer model (MAE = 0.4207, MSE = 0.3235), respectively, and further reduce MAE and MSE by 37.3% and 63.4%,respectively, compared with the basic SCINet model (MAE = 0.245, MSE = 0.246). These results verify the superiority and practical value of the proposed method in handling complex power forecasting tasks.

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