A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power Prediction
Interval prediction is essential to improve the scheduling and planning of wind power systems. In this study, a novel lower upper bound estimation model based on the gated recurrent unit was proposed for the clustered wind power forecasting. Different from existing research, the proposed model direc...
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2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9406581/ |
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doaj-4fe5c2c9ab47412abf27db199fbefab02021-04-28T23:00:35ZengIEEEIEEE Access2169-35362021-01-019617396175110.1109/ACCESS.2021.30739959406581A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power PredictionXiaosheng Peng0https://orcid.org/0000-0002-9958-7045Qiyou Xu1Hongyu Wang2Jianxun Lang3Wenze Li4Tao Cai5https://orcid.org/0000-0002-9244-8069Shanxu Duan6https://orcid.org/0000-0001-7329-2487Yuying Xie7https://orcid.org/0000-0001-7701-8637Chaoshun Li8https://orcid.org/0000-0003-1664-5877State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaState Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, ChinaChina-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, ChinaInterval prediction is essential to improve the scheduling and planning of wind power systems. In this study, a novel lower upper bound estimation model based on the gated recurrent unit was proposed for the clustered wind power forecasting. Different from existing research, the proposed model directly realizes interval prediction based on the point prediction results and the corresponding error interval coefficients, and an unsupervised learning strategy is introduced to construct the error interval coefficients. In addition, loss functions related to the characteristics of the prediction interval are designed, and an effective gradient descent algorithm is adopted to optimize the entire model. In the comparative experiments, two clustered data were collected as experimental data, and seven representative models were selected as benchmark models, which fully proved the superiority of the proposed model.https://ieeexplore.ieee.org/document/9406581/Gated recurrent unitlower upper bound estimationclustered wind power predictiondevised loss functionserror interval coefficients |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Xiaosheng Peng Qiyou Xu Hongyu Wang Jianxun Lang Wenze Li Tao Cai Shanxu Duan Yuying Xie Chaoshun Li |
| spellingShingle |
Xiaosheng Peng Qiyou Xu Hongyu Wang Jianxun Lang Wenze Li Tao Cai Shanxu Duan Yuying Xie Chaoshun Li A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power Prediction IEEE Access Gated recurrent unit lower upper bound estimation clustered wind power prediction devised loss functions error interval coefficients |
| author_facet |
Xiaosheng Peng Qiyou Xu Hongyu Wang Jianxun Lang Wenze Li Tao Cai Shanxu Duan Yuying Xie Chaoshun Li |
| author_sort |
Xiaosheng Peng |
| title |
A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power Prediction |
| title_short |
A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power Prediction |
| title_full |
A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power Prediction |
| title_fullStr |
A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power Prediction |
| title_full_unstemmed |
A Novel Efficient DLUBE Model Constructed by Error Interval Coefficients for Clustered Wind Power Prediction |
| title_sort |
novel efficient dlube model constructed by error interval coefficients for clustered wind power prediction |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2021-01-01 |
| description |
Interval prediction is essential to improve the scheduling and planning of wind power systems. In this study, a novel lower upper bound estimation model based on the gated recurrent unit was proposed for the clustered wind power forecasting. Different from existing research, the proposed model directly realizes interval prediction based on the point prediction results and the corresponding error interval coefficients, and an unsupervised learning strategy is introduced to construct the error interval coefficients. In addition, loss functions related to the characteristics of the prediction interval are designed, and an effective gradient descent algorithm is adopted to optimize the entire model. In the comparative experiments, two clustered data were collected as experimental data, and seven representative models were selected as benchmark models, which fully proved the superiority of the proposed model. |
| topic |
Gated recurrent unit lower upper bound estimation clustered wind power prediction devised loss functions error interval coefficients |
| url |
https://ieeexplore.ieee.org/document/9406581/ |
| work_keys_str_mv |
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