Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model
The accurate forecast of wind power is crucial for the stable operation and economic dispatch of renewable energy power systems. To improve the accuracy of ultra-short-term wind-power forecast, we propose an improved model combining a convolutional neural network (CNN), bidirectional long short-term...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2024-12-01
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| Series: | iEnergy |
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| Online Access: | https://www.sciopen.com/article/10.23919/IEN.2024.0026 |
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| _version_ | 1841550198464053248 |
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| author | Weilong Yu Shuaibing Li Hao Zhang Yongqiang Kang Hongwei Li Haiying Dong |
| author_facet | Weilong Yu Shuaibing Li Hao Zhang Yongqiang Kang Hongwei Li Haiying Dong |
| author_sort | Weilong Yu |
| collection | DOAJ |
| description | The accurate forecast of wind power is crucial for the stable operation and economic dispatch of renewable energy power systems. To improve the accuracy of ultra-short-term wind-power forecast, we propose an improved model combining a convolutional neural network (CNN), bidirectional long short-term memory, and an attention mechanism network. First, the basic principle of the proposed model is introduced along with its merits in ultra-short-term wind-power forecast. Then, relevant data are processed based on the Pearson similarity criterion, and relevant feature parameters for wind-power forecast are optimized. Finally, the proposed model is analyzed based on the public dataset of the Baidu KDD Cup 2022 wind-power forecast competition and actual data from a wind farm in Shandong. Results show that the proposed model can effectively overcome the shortcomings of traditional forecast methods in terms of overfitting, feature extraction, and parameter tuning. Furthermore, the model exhibits higher forecast accuracy and stability. |
| format | Article |
| id | doaj-art-863063c524f14d97a07c7a3b1adbb368 |
| institution | Kabale University |
| issn | 2771-9197 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | iEnergy |
| spelling | doaj-art-863063c524f14d97a07c7a3b1adbb3682025-01-10T06:52:44ZengTsinghua University PressiEnergy2771-91972024-12-013426828210.23919/IEN.2024.0026Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention modelWeilong Yu0Shuaibing Li1Hao Zhang2Yongqiang Kang3Hongwei Li4Haiying Dong5School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaCSSC Haizhuang Wind Power Co., Ltd, Chongqing 401120, ChinaSchool of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaThe accurate forecast of wind power is crucial for the stable operation and economic dispatch of renewable energy power systems. To improve the accuracy of ultra-short-term wind-power forecast, we propose an improved model combining a convolutional neural network (CNN), bidirectional long short-term memory, and an attention mechanism network. First, the basic principle of the proposed model is introduced along with its merits in ultra-short-term wind-power forecast. Then, relevant data are processed based on the Pearson similarity criterion, and relevant feature parameters for wind-power forecast are optimized. Finally, the proposed model is analyzed based on the public dataset of the Baidu KDD Cup 2022 wind-power forecast competition and actual data from a wind farm in Shandong. Results show that the proposed model can effectively overcome the shortcomings of traditional forecast methods in terms of overfitting, feature extraction, and parameter tuning. Furthermore, the model exhibits higher forecast accuracy and stability.https://www.sciopen.com/article/10.23919/IEN.2024.0026wind turbinesultra-short-term power forecastingconvolutional neural networkbidirectional long short-term memory networkattention mechanismsubtraction-average-based optimizer |
| spellingShingle | Weilong Yu Shuaibing Li Hao Zhang Yongqiang Kang Hongwei Li Haiying Dong Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model iEnergy wind turbines ultra-short-term power forecasting convolutional neural network bidirectional long short-term memory network attention mechanism subtraction-average-based optimizer |
| title | Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model |
| title_full | Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model |
| title_fullStr | Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model |
| title_full_unstemmed | Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model |
| title_short | Ultra-short-term wind-power forecasting based on an optimized CNN–BILSTM–attention model |
| title_sort | ultra short term wind power forecasting based on an optimized cnn bilstm attention model |
| topic | wind turbines ultra-short-term power forecasting convolutional neural network bidirectional long short-term memory network attention mechanism subtraction-average-based optimizer |
| url | https://www.sciopen.com/article/10.23919/IEN.2024.0026 |
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