Dynamic graph structure and spatio-temporal representations in wind power forecasting
Wind Power Forecasting (WPF) has gained considerable focus as a crucial aspect of the successful integration and operation of wind power. However, due to the stochastic and unstable nature of wind, it poses a real challenge to effectively analyze the correlations among multiple time series data for...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | Science and Technology for Energy Transition |
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| Online Access: | https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240214/stet20240214.html |
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| _version_ | 1841554645865988096 |
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| author | Zang Peng Dong Wenqi Wang Jing Fu Jianglong |
| author_facet | Zang Peng Dong Wenqi Wang Jing Fu Jianglong |
| author_sort | Zang Peng |
| collection | DOAJ |
| description | Wind Power Forecasting (WPF) has gained considerable focus as a crucial aspect of the successful integration and operation of wind power. However, due to the stochastic and unstable nature of wind, it poses a real challenge to effectively analyze the correlations among multiple time series data for accurate prediction. In our study, an end-to-end framework called Dynamic Graph structure and Spatio-Temporal representation learning (DSTG) framework is proposed to achieve stable power forecasting by constructing graph data to capture the critical features in the data. Specifically, a Graph Structure Learning (GSL) module is introduced to dynamically construct task-related correlation matrices via backpropagation to mitigate the inherent inconsistency and randomness of wind power data. Additionally, a dual-scale temporal graph learning (DTG) module is further proposed to explore the implicit spatio-temporal features at a fine-grained level using different skip connections from the constructed graph data. Finally, comprehensive experiments are performed on the collected Xuji Group Wind Power (XGWP) dataset, and the results show that DSTG outperforms the state-of-the-art spatio-temporal methods by 10.12% on the average of root mean square error and mean absolute error, demonstrating the effectiveness of DSTG. In conclusion, our model provides a promising approach. |
| format | Article |
| id | doaj-art-9481ebbfb6b042e6bbbfcca329ad392b |
| institution | Kabale University |
| issn | 2804-7699 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Science and Technology for Energy Transition |
| spelling | doaj-art-9481ebbfb6b042e6bbbfcca329ad392b2025-01-08T11:24:01ZengEDP SciencesScience and Technology for Energy Transition2804-76992025-01-0180910.2516/stet/2024100stet20240214Dynamic graph structure and spatio-temporal representations in wind power forecastingZang Peng0Dong Wenqi1Wang Jing2Fu Jianglong3State Grid Jibei Zhangjiakou Wind, PV, Storage and Transmission Renewable Energy Co., Ltd.State Grid Jibei Zhangjiakou Wind, PV, Storage and Transmission Renewable Energy Co., Ltd.State Grid Jibei Zhangjiakou Wind, PV, Storage and Transmission Renewable Energy Co., Ltd.Hebei University of Architecture, Information Engineering CollegeWind Power Forecasting (WPF) has gained considerable focus as a crucial aspect of the successful integration and operation of wind power. However, due to the stochastic and unstable nature of wind, it poses a real challenge to effectively analyze the correlations among multiple time series data for accurate prediction. In our study, an end-to-end framework called Dynamic Graph structure and Spatio-Temporal representation learning (DSTG) framework is proposed to achieve stable power forecasting by constructing graph data to capture the critical features in the data. Specifically, a Graph Structure Learning (GSL) module is introduced to dynamically construct task-related correlation matrices via backpropagation to mitigate the inherent inconsistency and randomness of wind power data. Additionally, a dual-scale temporal graph learning (DTG) module is further proposed to explore the implicit spatio-temporal features at a fine-grained level using different skip connections from the constructed graph data. Finally, comprehensive experiments are performed on the collected Xuji Group Wind Power (XGWP) dataset, and the results show that DSTG outperforms the state-of-the-art spatio-temporal methods by 10.12% on the average of root mean square error and mean absolute error, demonstrating the effectiveness of DSTG. In conclusion, our model provides a promising approach.https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240214/stet20240214.htmlgraphwind powerspatio-temporal featuresgraph neural networksdynamic graph structure |
| spellingShingle | Zang Peng Dong Wenqi Wang Jing Fu Jianglong Dynamic graph structure and spatio-temporal representations in wind power forecasting Science and Technology for Energy Transition graph wind power spatio-temporal features graph neural networks dynamic graph structure |
| title | Dynamic graph structure and spatio-temporal representations in wind power forecasting |
| title_full | Dynamic graph structure and spatio-temporal representations in wind power forecasting |
| title_fullStr | Dynamic graph structure and spatio-temporal representations in wind power forecasting |
| title_full_unstemmed | Dynamic graph structure and spatio-temporal representations in wind power forecasting |
| title_short | Dynamic graph structure and spatio-temporal representations in wind power forecasting |
| title_sort | dynamic graph structure and spatio temporal representations in wind power forecasting |
| topic | graph wind power spatio-temporal features graph neural networks dynamic graph structure |
| url | https://www.stet-review.org/articles/stet/full_html/2025/01/stet20240214/stet20240214.html |
| work_keys_str_mv | AT zangpeng dynamicgraphstructureandspatiotemporalrepresentationsinwindpowerforecasting AT dongwenqi dynamicgraphstructureandspatiotemporalrepresentationsinwindpowerforecasting AT wangjing dynamicgraphstructureandspatiotemporalrepresentationsinwindpowerforecasting AT fujianglong dynamicgraphstructureandspatiotemporalrepresentationsinwindpowerforecasting |