A review of federated learning in renewable energy applications: Potential, challenges, and future directions

TL;DR

This paper reviews federated learning's potential and challenges in renewable energy, highlighting its ability to enable privacy-preserving collaborative data analysis and outlining future research directions.

Contribution

It provides a comprehensive survey of federated learning applications in renewable energy, discussing algorithms, case studies, and future research opportunities.

Findings

Federated learning enhances data privacy in renewable energy applications.

It enables collaborative analysis without sharing raw data.

Challenges include data heterogeneity and communication costs.

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.