Application of a Federated Learning-Based Distributed PV Output Prediction Model in Distribution Networks

This study investigates the application potential of a federated learning-based distributed PV output prediction model in distribution networks to address the challenges posed by PV output fluctuations to grid stability, while ensuring data privacy protection. Using a city-level industrial park distribution network as the research subject, the study analyzes the operational characteristics of distributed PV systems and multi-source influencing factors. A prediction model integrating long-term and short-term memory networks is constructed, a distributed collaborative training mechanism under the federated averaging algorithm is designed, and technical solutions to enhance prediction accuracy are proposed. The research results demonstrate that this model can effectively integrate multi-source information while protecting data privacy at each node, achieving precise PV output prediction.