A power load forecasting method using cosine similarity and a graph convolutional network - JI Shan; JIANG Wei; JING Xin (Zhejiang dianli)
To address the challenges of existing power load forecasting models， which struggle to deeply extract spatiotemporal correlation features and exhibit weak generalization capabilities—failing to simultaneously manage both short-term and long-term forecasting—this study proposes a multi-user power load forecasting method using cosine similarity and a global-local collaborative graph convolutional network. First， cosine similarity is utilized to learn similar patterns between load data from different nodes， allowing for the deep extraction of spatiotemporal correlation features. Second， a collaborative modeling approach is applied to static global factors and dynamic local factors that influence power load trends， enhancing the model’s generalization ability. Finally， extensive experiments on a real-world dataset demonstrate the method’s effectiveness and robustness in forecasting both short-term and long-term load series.