Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

doi:10.1016/j.dcan.2022.03.022

	Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting
	Zhang, Weishan 1; Chen, Xiao1 ; He, Ke 2; Chen, Leiming 1; Xu, Liang 3; Wang, Xiao4 ; Yang, Su 5
发表期刊	DIGITAL COMMUNICATIONS AND NETWORKS
ISSN	2468-5925
	2023-10-01
卷号	9 期号:5 页码:1221-1229
通讯作者	Zhang, Weishan(zhangws@upc.edu.cn)
摘要	Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power grids. Existing deep-learning-based methods can perform well if there are sufficient training data and enough computational resources. However, there are challenges in building models through centralized shared data due to data privacy concerns and industry competition. Federated learning is a new distributed machine learning approach which enables training models across edge devices while data reside locally. In this paper, we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM model. We design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting approach. Thorough evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy, and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.
关键词	Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM
DOI	10.1016/j.dcan.2022.03.022
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62072469] ; National Key R & D Program of China[2018AAA0101502] ; Shandong Natural Science Foundation[ZR2019MF049] ; West Coast artificial intelligence technology innovation center[2019-1-5] ; West Coast artificial intelligence technology innovation center[2019-1-6] ; Opening Project of Shanghai Trusted Industrial Control Platform[TICPSH202003015-ZC]
项目资助者	National Natural Science Foundation of China ; National Key R & D Program of China ; Shandong Natural Science Foundation ; West Coast artificial intelligence technology innovation center ; Opening Project of Shanghai Trusted Industrial Control Platform
WOS研究方向	Telecommunications
WOS类目	Telecommunications
WOS记录号	WOS:001107446900001
出版者	KEAI PUBLISHING LTD
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/55232
专题	多模态人工智能系统全国重点实验室
通讯作者	Zhang, Weishan
作者单位	1.China Univ Petr East China, Coll Comp Sci & Technol, Dongying, Peoples R China 2.Tsinghua Univ, Sichuan Energy Internet Res Inst, Beijing, Peoples R China 3.Beijing Univ Sci & Technol, Sch Comp & Commun Engn, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 5.Fudan Univ, Sch Comp Sci, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Weishan,Chen, Xiao,He, Ke,et al. Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting[J]. DIGITAL COMMUNICATIONS AND NETWORKS,2023,9(5):1221-1229.
APA	Zhang, Weishan.,Chen, Xiao.,He, Ke.,Chen, Leiming.,Xu, Liang.,...&Yang, Su.(2023).Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting.DIGITAL COMMUNICATIONS AND NETWORKS,9(5),1221-1229.
MLA	Zhang, Weishan,et al."Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting".DIGITAL COMMUNICATIONS AND NETWORKS 9.5(2023):1221-1229.