Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets. By preserving data privacy, federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation, research and development. Our paper provides an overview of federated learning in renewable energy applications. We discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and consumption. We also evaluate the potential and the challenges associated with federated learning applied in power and energy contexts. Finally, we outline promising future research directions in federated learning for applications in renewable energy.

翻译：联邦学习最近兴起为一种保护隐私的分布式机器学习方法。联邦学习能够在不共享训练数据集的情况下，实现多个客户端及整个设备群的协同训练。通过保护数据隐私，联邦学习有潜力克服可再生能源领域因缺乏数据共享而抑制创新、研发的现状。本文概述了联邦学习在可再生能源中的应用。我们探讨了联邦学习算法，并综述了其在可再生能源发电与消费领域的应用案例研究。同时，评估了联邦学习在电力能源场景中应用的潜力与相关挑战。最后，展望了联邦学习在可再生能源应用中有前景的未来研究方向。