A Study on Interpretable Electric Load Forecasting Model with Spatiotemporal Feature Fusion Based on Attention Mechanism

• Published in: Technologies (Basel) Vol. 13; no. 6; p. 219
• Main Authors: Li, Shuaishuai, Chen, Weizhen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: Accuracy; Algorithms; Alternative energy sources; attention mechanism; China; Decision making; Decoupling; Deep learning; Electric power grids; Electric power systems; Electrical loads; Electricity; Electricity pricing; Feature extraction; Forecasting; High temperature; interpretability; Machine learning; Modelling; Neural networks; power load forecasting; Prediction theory; Renewable energy; Renewable resources; Root-mean-square errors; spatiotemporal feature fusion; Time series; Wind power; China

Driven by the global “double carbon” goal, the volatility of renewable energy poses a challenge to the stability of power systems. Traditional methods have difficulty dealing with high-dimensional nonlinear data, and the single deep learning model has the limitations of spatiotemporal feature decoup...