Spatio–Temporal Aware Personalized Federated Learning for Load Forecasting in Power Systems

With the development of smart grids and the increasing demand for electric energy consumption, electricity load forecasting has become more and more important in electric energy management. However, the differences in electricity load patterns between different regions lead to data heterogeneity, which may seriously affect the model performance of traditional federated learning methods in electricity load forecasting. Meanwhile, the resource heterogeneity between clients will further decrease the efficiency and the accuracy of forecasting. To this end, we propose a spatio–temporal aware personalized federated learning (PFL) framework for electricity load forecasting to improve the forecasting accuracy and training speed so as to enhance the real-time responsiveness and system stability of the power grid. First, to solve the data heterogeneity, we design a collaborative training domain (CTD) construction method based on spatio–temporal features. Then, on the basis of constructed CTDs, we propose a spatio–temporal convolutional network (STCN)-based layered PFL method to address the resource heterogeneity, which can be divided into personalized layers and generalized layers according to temporal and spatial static features separately. In addition, we design a hierarchical aggregation mechanism and an adaptive edge model aggregation adjustment mechanism to optimize the training process. Experimental results show that the method proposed in this article outperforms other methods in terms of model accuracy and convergence speed.