可再生能源应用中的联合学习综述:潜力、挑战和未来方向

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-05-08 DOI:10.1016/j.egyai.2024.100375
Albin Grataloup , Stefan Jonas , Angela Meyer
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引用次数: 0

摘要

最近,联盟学习作为一种保护隐私的分布式机器学习方法出现了。联盟学习可以在不共享相关训练数据集的情况下,对多个客户和整个机群进行协作训练。通过保护数据隐私,联合学习有可能克服可再生能源领域缺乏数据共享这一阻碍创新、研究和开发的问题。本文概述了联合学习在可再生能源领域的应用。我们讨论了联合学习算法,并调查了它们在可再生能源生产和消费中的应用和案例研究。我们还评估了联合学习在电力和能源领域应用的潜力和挑战。最后,我们概述了联合学习在可再生能源应用中的未来研究方向。
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A review of federated learning in renewable energy applications: Potential, challenges, and future directions

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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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
期刊最新文献
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