A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms
Accurate forecasting of renewable energy generation is the foundation for all renewable energy consumption technologies. Currently, wind power forecasting techniques have become relatively mature under normal weather conditions. However, under extreme weather conditions, the difficulty of research i...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIP Publishing LLC
2025-04-01
|
| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0250465 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850281699453173760 |
|---|---|
| author | Bo Wang Shu Wang Zheng Wang Yingying Zheng Xin Li |
| author_facet | Bo Wang Shu Wang Zheng Wang Yingying Zheng Xin Li |
| author_sort | Bo Wang |
| collection | DOAJ |
| description | Accurate forecasting of renewable energy generation is the foundation for all renewable energy consumption technologies. Currently, wind power forecasting techniques have become relatively mature under normal weather conditions. However, under extreme weather conditions, the difficulty of research is caused by data scarcity and significant weather fluctuations. Here, this paper proposes a wind power forecasting framework that considers small sample expansion and hybrid deep learning algorithms. First, the forecasting framework selects sensitive meteorological factors through the shapley additive explanations (SHAP) theory, reducing the redundancy of model input features. Simultaneously, a conditional generative adversarial network based on discriminant weights is employed to expand small samples of extreme weather data, overcoming the issue of data scarcity and improving model training efficiency. Finally, a hybrid deep learning model, the convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) network-attention mechanism (AM), is constructed. This model combines the CNN and BiLSTM network to capture local features and long-term temporal trends complementarily and further enhances the learning of key temporal features through an AM, finally outputting the forecasting results. Through case analysis on a wind power dataset in Liaoning, China, the experimental results show that compared with traditional forecasting methods, the proposed framework reduces the mean absolute error (MAE) by 52.29% and increases R2 by 0.0625 under windy conditions, and reduces the MAE by 64.29% and increases R2 by 0.0462 under low-temperature weather. This indicates that the proposed wind power forecasting framework can effectively improve the forecasting accuracy under extreme weather conditions. |
| format | Article |
| id | doaj-art-7e33d88e87d546a2b2b6ad99ebc34593 |
| institution | OA Journals |
| issn | 2158-3226 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-7e33d88e87d546a2b2b6ad99ebc345932025-08-20T01:48:12ZengAIP Publishing LLCAIP Advances2158-32262025-04-01154045015045015-1510.1063/5.0250465A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithmsBo Wang0Shu Wang1Zheng Wang2Yingying Zheng3Xin Li4National Key Laboratory of Renewable Energy Grid-Integration, Beijing 100192, ChinaNational Key Laboratory of Renewable Energy Grid-Integration, Beijing 100192, ChinaNational Key Laboratory of Renewable Energy Grid-Integration, Beijing 100192, ChinaChina Agricultural University, Beijing 100083, ChinaChina Agricultural University, Beijing 100083, ChinaAccurate forecasting of renewable energy generation is the foundation for all renewable energy consumption technologies. Currently, wind power forecasting techniques have become relatively mature under normal weather conditions. However, under extreme weather conditions, the difficulty of research is caused by data scarcity and significant weather fluctuations. Here, this paper proposes a wind power forecasting framework that considers small sample expansion and hybrid deep learning algorithms. First, the forecasting framework selects sensitive meteorological factors through the shapley additive explanations (SHAP) theory, reducing the redundancy of model input features. Simultaneously, a conditional generative adversarial network based on discriminant weights is employed to expand small samples of extreme weather data, overcoming the issue of data scarcity and improving model training efficiency. Finally, a hybrid deep learning model, the convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) network-attention mechanism (AM), is constructed. This model combines the CNN and BiLSTM network to capture local features and long-term temporal trends complementarily and further enhances the learning of key temporal features through an AM, finally outputting the forecasting results. Through case analysis on a wind power dataset in Liaoning, China, the experimental results show that compared with traditional forecasting methods, the proposed framework reduces the mean absolute error (MAE) by 52.29% and increases R2 by 0.0625 under windy conditions, and reduces the MAE by 64.29% and increases R2 by 0.0462 under low-temperature weather. This indicates that the proposed wind power forecasting framework can effectively improve the forecasting accuracy under extreme weather conditions.http://dx.doi.org/10.1063/5.0250465 |
| spellingShingle | Bo Wang Shu Wang Zheng Wang Yingying Zheng Xin Li A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms AIP Advances |
| title | A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms |
| title_full | A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms |
| title_fullStr | A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms |
| title_full_unstemmed | A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms |
| title_short | A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms |
| title_sort | method for short term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms |
| url | http://dx.doi.org/10.1063/5.0250465 |
| work_keys_str_mv | AT bowang amethodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT shuwang amethodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT zhengwang amethodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT yingyingzheng amethodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT xinli amethodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT bowang methodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT shuwang methodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT zhengwang methodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT yingyingzheng methodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms AT xinli methodforshorttermwindpowerforecastingunderextremeweatherconditionsbasedonmeteorologicalfactorinterpretabilityandhybriddeeplearningalgorithms |