Federated learning and non-federated learning based power forecasting of photovoltaic/wind power energy systems: A systematic review

Renewable energy sources, particularly photovoltaic and wind power, are essential in meeting global energy demands while minimizing environmental impact. Accurate photovoltaic (PV) and wind power (WP) forecasting is crucial for effective grid management and sustainable energy integration. However, traditional forecasting methods encounter challenges such as data privacy, centralized processing, and data sharing, particularly with dispersed data sources. This review paper thoroughly examines the necessity of forecasting models, methodologies, and data integrity, with a keen eye on the evolving landscape of Federated Learning (FL) in PV and WP forecasting. Commencing with an introduction highlighting the significance of forecasting models in optimizing renewable energy resource utilization, the paper delves into various forecasting techniques and emphasizes the critical need for data integrity and security. A comprehensive overview of non-federated learning-based PV and WP forecasting is presented based on high-quality journals, followed by in-depth discussions on specific non-federated learning approaches for each power source. The paper subsequently introduces FL and its variants, including horizontal, vertical, transfer, cross-device, and cross-silo FL, highlighting the crucial role of encryption mechanisms and addressing associated challenges. Furthermore, drawing on extensive investigations of numerous pertinent articles, the paper outlines the innovative horizon of FL-based PV and wind power forecasting, offering insights into FL-based methodologies and concluding with observations drawn from this frontier.