Towards Federated Learning-Based Forecasting of Renewable Energy Production

The integration of volatile renewable energy sources requires reliable generation forecasts. Traditional forecasting methods that rely on commercial providers impose costs and de-pendencies on renewable energy operators. This paper proposes a literature survey on federated learning (FL) in the context of renewable energy forecasting and an analysis of open challenges in research and practice and possible solution approaches for re-alizing such a framework. Our focus is on short-term forecasts for day-ahead markets, which are critical for trading and operational efficiency. The FL approach preserves data privacy and improves forecast accuracy by leveraging distributed data from multiple operators. We present an analysis of current FL applications in renewable energy forecasting, identify implementation challenges, and propose solutions to overcome these barriers. This study aims to empower market participants to produce independent, accurate forecasts, thereby improving economical outcomes and operational stability.