The study focuses on federated learning for short-term load forecasting (STLF), addressing data privacy concerns. The authors identify structured heterogeneity in clients' load data: different responses to exogenous factors and distinct temporal load profiles, which degrade forecasting performance in federated learning. To mitigate these issues, they propose two model initialization strategies — global and local — that improve forecasting accuracy.
Relevant for forecasting tasks in the energy sector where distributed data and privacy must be considered, and adaptation to customer heterogeneity is needed.
Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.
