A review of federated learning in renewable energy applications: Potential, challenges, and future directions
Albin Grataloup,
Stefan Jonas,
Angela Meyer
Affiliations
Albin Grataloup
Bern University of Applied Sciences, School of Engineering and Computer Science, Quellgasse 21, Biel, 2501, Switzerland
Stefan Jonas
Bern University of Applied Sciences, School of Engineering and Computer Science, Quellgasse 21, Biel, 2501, Switzerland; Università della Svizzera italiana, Faculty of Informatics, Via la Santa 1, Lugano-Viganello, 6962, Switzerland; Corresponding author at: Bern University of Applied Sciences, School of Engineering and Computer Science, Quellgasse 21, Biel, 2501, Switzerland.
Angela Meyer
Bern University of Applied Sciences, School of Engineering and Computer Science, Quellgasse 21, Biel, 2501, Switzerland; Delft University of Technology, Department of Geoscience and Remote Sensing, Stevinweg 1, Delft, 2628, The Netherlands
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.
