Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting
文献类型：期刊论文
| 作者 | Zhang, Weishan1; Chen, Xiao1 ; He, Ke2; Chen, Leiming1; Xu, Liang3; Wang, Xiao4; Yang, Su5
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| 刊名 | DIGITAL COMMUNICATIONS AND NETWORKS
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| 出版日期 | 2023-10-01 |
| 卷号 | 9期号:5页码:1221-1229 |
| 关键词 | Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM |
| ISSN号 | 2468-5925 |
| DOI | 10.1016/j.dcan.2022.03.022 |
| 通讯作者 | Zhang, Weishan(zhangws@upc.edu.cn) |
| 英文摘要 | 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. |
| 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] |
| WOS研究方向 | Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001107446900001 |
| 出版者 | KEAI PUBLISHING LTD |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55231]  |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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