Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm
文献类型：期刊论文
| 作者 | Chen, Xiaojiao1 ; Zhang, Xiuqing1; Dong, Mi2; Huang, Liansheng1; Guo, Yan2; He, Shiying1
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| 刊名 | FRONTIERS IN ENERGY RESEARCH
 |
| 出版日期 | 2021-07-29 |
| 卷号 | 9 |
| ISSN号 | 2296-598X |
| 关键词 | wind farm wind turbine convolutional neural network long short-term memory network spatiotemporal power prediction |
| DOI | 10.3389/fenrg.2021.723775 |
| 通讯作者 | Dong, Mi(mi.dong@csu.edu.cn) ; Huang, Liansheng(huangls@ipp.ac.cn) |
| 英文摘要 | The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Starting from the time series of wind power, it is present a two-stage modeling strategy, in which a deep neural network combines spatiotemporal correlation to simultaneously predict the power of multiple wind turbines. Specifically, the network is a joint model composed of Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN). Herein, the LSTM captures the temporal dependence of the historical power sequence, while the CNN extracts the spatial features among the data, thereby achieving the power prediction for multiple wind turbines. The proposed approach is validated by using the wind power data from an offshore wind farm in China, and the results in comparison with other approaches shows the high prediction preciseness achieved by the proposed approach. |
| WOS关键词 | PV SYSTEMS |
| 资助项目 | Major Science and Technology Projects in Anhui Province[202003a05020019] ; Comprehensive Research Facility for Fusion Technology[2018-000052-73-01-001228] |
| WOS研究方向 | Energy & Fuels |
| 语种 | 英语 |
| 出版者 | FRONTIERS MEDIA SA |
| WOS记录号 | WOS:000684982800001 |
| 资助机构 | Major Science and Technology Projects in Anhui Province ; Comprehensive Research Facility for Fusion Technology |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/124260]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Dong, Mi; Huang, Liansheng |
| 作者单位 | 1.Chinese Acad Sci, Inst Plasma Phys, Hefei, Peoples R China 2.Cent South Univ, Sch Automat, Changsha, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Xiaojiao,Zhang, Xiuqing,Dong, Mi,et al. Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm[J]. FRONTIERS IN ENERGY RESEARCH,2021,9. |
| APA | Chen, Xiaojiao,Zhang, Xiuqing,Dong, Mi,Huang, Liansheng,Guo, Yan,&He, Shiying.(2021).Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm.FRONTIERS IN ENERGY RESEARCH,9. |
| MLA | Chen, Xiaojiao,et al."Deep Learning-Based Prediction of Wind Power for Multi-turbines in a Wind Farm".FRONTIERS IN ENERGY RESEARCH 9(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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