A review of federated learning in renewable energy applications
Potential, challenges, and future directions
Albin Grataloup (Bern University of Applied Sciences)
S. Jonas (University of Lugano, Bern University of Applied Sciences)
A. Meyer (TU Delft - Atmospheric Remote Sensing, Bern University of Applied Sciences)
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Abstract
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.
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