Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting
In order to make full use of the prior relationships among data features and improve the prediction accuracy of medium and long term wind power at wind farms, a medium and long term wind power prediction model based on graph convolution neural network (GCN), wind velocity differential fitting (DF),...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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State Grid Energy Research Institute
2023-08-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303050 |
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| _version_ | 1850070266919518208 |
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| author | Zihan CHEN Wei TENG Xuefeng XU Xian DING Yibing LIU |
| author_facet | Zihan CHEN Wei TENG Xuefeng XU Xian DING Yibing LIU |
| author_sort | Zihan CHEN |
| collection | DOAJ |
| description | In order to make full use of the prior relationships among data features and improve the prediction accuracy of medium and long term wind power at wind farms, a medium and long term wind power prediction model based on graph convolution neural network (GCN), wind velocity differential fitting (DF), and particle swarm optimization (PSO) is proposed. By analyzing the whole process of wind power generation, the influencing factors of wind power and the interrelation among them are explored, and the GCN model is built. The wind velocity and power utilization efficiency are fitted respectively. The wind power is calculated by combining with the wind velocity–power calculation model based on DF. The loss of the model includes three parts: power loss, wind velocity loss and power utilization efficiency loss. PSO algorithm is used to determine the appropriate weight for the three losses. The on-site examples of two wind farms show that the relative root mean square error of the wind power prediction model in the next 10 days is 11.44% and 13.09%, respectively, which has a high prediction accuracy. |
| format | Article |
| id | doaj-art-5a6802b9c9f843f3bcc399bc39adcf9d |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-08-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-5a6802b9c9f843f3bcc399bc39adcf9d2025-08-20T02:47:35ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-08-0156109610510.11930/j.issn.1004-9649.202303050zgdl-56-10-chenzihanMedium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential FittingZihan CHEN0Wei TENG1Xuefeng XU2Xian DING3Yibing LIU4Key Laboratory of Power Station Energy Transfer, Conversion and System, Ministry of Education, North China Electric Power University, Beijing 102206, ChinaKey Laboratory of Power Station Energy Transfer, Conversion and System, Ministry of Education, North China Electric Power University, Beijing 102206, ChinaChina Green Development Investment Group Co., Ltd., Beijing 100020, ChinaChina Green Development Investment Group Co., Ltd., Beijing 100020, ChinaKey Laboratory of Power Station Energy Transfer, Conversion and System, Ministry of Education, North China Electric Power University, Beijing 102206, ChinaIn order to make full use of the prior relationships among data features and improve the prediction accuracy of medium and long term wind power at wind farms, a medium and long term wind power prediction model based on graph convolution neural network (GCN), wind velocity differential fitting (DF), and particle swarm optimization (PSO) is proposed. By analyzing the whole process of wind power generation, the influencing factors of wind power and the interrelation among them are explored, and the GCN model is built. The wind velocity and power utilization efficiency are fitted respectively. The wind power is calculated by combining with the wind velocity–power calculation model based on DF. The loss of the model includes three parts: power loss, wind velocity loss and power utilization efficiency loss. PSO algorithm is used to determine the appropriate weight for the three losses. The on-site examples of two wind farms show that the relative root mean square error of the wind power prediction model in the next 10 days is 11.44% and 13.09%, respectively, which has a high prediction accuracy.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303050wind power generationwind power predictiongraph convolutional neural networkwind velocity differential fittingparticle swarm optimization algorithm |
| spellingShingle | Zihan CHEN Wei TENG Xuefeng XU Xian DING Yibing LIU Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting Zhongguo dianli wind power generation wind power prediction graph convolutional neural network wind velocity differential fitting particle swarm optimization algorithm |
| title | Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting |
| title_full | Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting |
| title_fullStr | Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting |
| title_full_unstemmed | Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting |
| title_short | Medium and Long Term Wind Power Prediction Based on Graph Convolutional Network and Wind Velocity Differential Fitting |
| title_sort | medium and long term wind power prediction based on graph convolutional network and wind velocity differential fitting |
| topic | wind power generation wind power prediction graph convolutional neural network wind velocity differential fitting particle swarm optimization algorithm |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303050 |
| work_keys_str_mv | AT zihanchen mediumandlongtermwindpowerpredictionbasedongraphconvolutionalnetworkandwindvelocitydifferentialfitting AT weiteng mediumandlongtermwindpowerpredictionbasedongraphconvolutionalnetworkandwindvelocitydifferentialfitting AT xuefengxu mediumandlongtermwindpowerpredictionbasedongraphconvolutionalnetworkandwindvelocitydifferentialfitting AT xianding mediumandlongtermwindpowerpredictionbasedongraphconvolutionalnetworkandwindvelocitydifferentialfitting AT yibingliu mediumandlongtermwindpowerpredictionbasedongraphconvolutionalnetworkandwindvelocitydifferentialfitting |