A multi-factor collaborative electricity load forecasting method based on feature importance and multi-scale feature extraction

Abstract

In power systems, environmental fluctuations and electricity price volatility introduce uncertainties in user energy consumption behaviors, posing significant challenges to reliable energy planning. Existing studies often overlook the coupled relationships between the importance and correlations of multiple complex variables, lack consideration of the weighting and distribution of multi-dimensional features across multi-scale spaces, and fall short in multi-scale extraction and fusion of complex spatiotemporal characteristics. To address these issues, this paper proposes a multi-factor collaborative load forecasting method based on feature importance and multi-scale feature extraction. First, a novel evaluation model integrating feature importance and correlation is developed, and a comprehensive feature importance assessment method is proposed. Then, a multi-dimensional weighting extraction framework is designed, from which a multi-dimensional weight matrix and its multi-layer input structure are constructed. Finally, a multi-scale fusion model driven by a multi-channel convolutional neural network is developed. The backbone network is a multi-channel convolutional structure, consisting of a multi-level feature extraction module in the front, a multi-scale sampling mechanism in the middle, and a multi-scale feature fusion architecture in the rear. Based on the proposed comprehensive feature importance assessment method, a multi-factor collaborative load forecasting model is established, achieving accurate load prediction. Experimental results demonstrate that, compared with various state-of-the-art forecasting models, the proposed method reduces Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) by up to 28.30 %, 24.14 %, and 30.35 %, respectively.