A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction
The uncertainty of wind energy due to its non-stationary and random nature poses a major challenge to engineers responsible for power system scheduling. In the present research, a spatio-temporal graph deep neural network is used to learn the spatial characteristics and explore the strong correlatio...
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| Vydané v: | Energy (Oxford) Ročník 281; s. 128289 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.10.2023
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| ISSN: | 0360-5442 |
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| Abstract | The uncertainty of wind energy due to its non-stationary and random nature poses a major challenge to engineers responsible for power system scheduling. In the present research, a spatio-temporal graph deep neural network is used to learn the spatial characteristics and explore the strong correlation characteristics of wind-farm clusters. The Bagging, long short-term memory (LSTM), and random forest (RF) are superimposed based on the stacking integration algorithm. By analyzing the decomposed wind electron sequence characteristics, two Stacking models are used to predict the wind power of target wind-farms respectively and then a dual stacking model fusion strategy is formed. Finally, a multi-step prediction model with the spatio-temporal characteristics of D-stacking multi-integration fusion is designed for rolling multi-step prediction of wind-farm cluster power to obtain high-precision target wind-farm power. Through the actual wind power generation measured in northwest China to conduct case studies and comparative tests, it concludes that：(1) Spatio-temporal method in this paper can effectively extract the deep spatial features of wind farm clusters. (2) Dual fusion strategy improves Stacking effectively. (3) The proposed model can obtain accurate wind power prediction results, which is superior to 14 comparative algorithms proposed by other researchers.
•The spatio-temporal graph network is proposed to capture spatiotemporal features.•An integration algorithm for a multi-integration overlay framework is investigated.•The D-stacking spatio-temporal feature multi-step prediction model is proposed.•The dual Stacking model fusion strategy is proposed to improve the stacking algorithm.•The proposed method provides more accurate predictions than other methods. |
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| AbstractList | The uncertainty of wind energy due to its non-stationary and random nature poses a major challenge to engineers responsible for power system scheduling. In the present research, a spatio-temporal graph deep neural network is used to learn the spatial characteristics and explore the strong correlation characteristics of wind-farm clusters. The Bagging, long short-term memory (LSTM), and random forest (RF) are superimposed based on the stacking integration algorithm. By analyzing the decomposed wind electron sequence characteristics, two Stacking models are used to predict the wind power of target wind-farms respectively and then a dual stacking model fusion strategy is formed. Finally, a multi-step prediction model with the spatio-temporal characteristics of D-stacking multi-integration fusion is designed for rolling multi-step prediction of wind-farm cluster power to obtain high-precision target wind-farm power. Through the actual wind power generation measured in northwest China to conduct case studies and comparative tests, it concludes that：(1) Spatio-temporal method in this paper can effectively extract the deep spatial features of wind farm clusters. (2) Dual fusion strategy improves Stacking effectively. (3) The proposed model can obtain accurate wind power prediction results, which is superior to 14 comparative algorithms proposed by other researchers.
•The spatio-temporal graph network is proposed to capture spatiotemporal features.•An integration algorithm for a multi-integration overlay framework is investigated.•The D-stacking spatio-temporal feature multi-step prediction model is proposed.•The dual Stacking model fusion strategy is proposed to improve the stacking algorithm.•The proposed method provides more accurate predictions than other methods. The uncertainty of wind energy due to its non-stationary and random nature poses a major challenge to engineers responsible for power system scheduling. In the present research, a spatio-temporal graph deep neural network is used to learn the spatial characteristics and explore the strong correlation characteristics of wind-farm clusters. The Bagging, long short-term memory (LSTM), and random forest (RF) are superimposed based on the stacking integration algorithm. By analyzing the decomposed wind electron sequence characteristics, two Stacking models are used to predict the wind power of target wind-farms respectively and then a dual stacking model fusion strategy is formed. Finally, a multi-step prediction model with the spatio-temporal characteristics of D-stacking multi-integration fusion is designed for rolling multi-step prediction of wind-farm cluster power to obtain high-precision target wind-farm power. Through the actual wind power generation measured in northwest China to conduct case studies and comparative tests, it concludes that：(1) Spatio-temporal method in this paper can effectively extract the deep spatial features of wind farm clusters. (2) Dual fusion strategy improves Stacking effectively. (3) The proposed model can obtain accurate wind power prediction results, which is superior to 14 comparative algorithms proposed by other researchers. |
| ArticleNumber | 128289 |
| Author | Hou, Xinxing Li, Jian Gui, Jianglin Qu, Zhijian |
| Author_xml | – sequence: 1 givenname: Zhijian surname: Qu fullname: Qu, Zhijian – sequence: 2 givenname: Jian surname: Li fullname: Li, Jian email: 2021028080800005@ecjtu.edu.cn – sequence: 3 givenname: Xinxing surname: Hou fullname: Hou, Xinxing – sequence: 4 givenname: Jianglin surname: Gui fullname: Gui, Jianglin |
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| Keywords | Improved stacking Spatio-temporal graph deep neural network Multi-integration fusion Multi-step rolling forecast Wind power forecasting |
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| SubjectTerms | algorithms China energy Improved stacking Multi-integration fusion Multi-step rolling forecast neural networks power generation prediction Spatio-temporal graph deep neural network uncertainty wind wind farms wind power Wind power forecasting |
| Title | A D-stacking dual-fusion, spatio-temporal graph deep neural network based on a multi-integrated overlay for short-term wind-farm cluster power multi-step prediction |
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