Privacy-preserving federated learning for residential short-term load forecasting

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these as they provide detailed load data. However, using smart meter data forecasting is challenging due to privacy requirements. This paper investigates how requirements be addressed through a combination federated learning preserving techniques such differential secure aggregation. For our analysis, we employ large set simulate different models affect performance privacy. Our simulations reveal that combining both accuracy near-complete Specifically, find combinations enable level information sharing while ensuring the processed models. Moreover, identify discuss challenges applying learning, aggregation short-term forecasting.