A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization
Wind power prediction (WPP) is necessary to the safe operation and economic dispatch of power systems. In order to improve the prediction accuracy of WPP, in this paper we propose a three-step model named SDAE-SVR-BA to be applied in short-term WPP based on stacked-denoising-autoencoder (SDAE) featu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9955524/ |
| _version_ | 1797987585397620736 |
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| author | Ruiqin Duan Xiaosheng Peng Cong Li Zimin Yang Yan Jiang Xiufeng Li Shuangquan Liu |
| author_facet | Ruiqin Duan Xiaosheng Peng Cong Li Zimin Yang Yan Jiang Xiufeng Li Shuangquan Liu |
| author_sort | Ruiqin Duan |
| collection | DOAJ |
| description | Wind power prediction (WPP) is necessary to the safe operation and economic dispatch of power systems. In order to improve the prediction accuracy of WPP, in this paper we propose a three-step model named SDAE-SVR-BA to be applied in short-term WPP based on stacked-denoising-autoencoder (SDAE) feature processing, bat algorithm (BA) optimization and support vector regression (SVR). First, we preprocessed the original NWP data input into the SDAE-SVR-BA model to adapt to the training and prediction of the proposed model. Second, we input the preprocessed features into the SDAE network, whose parameters are optimized by BA to obtain the depth-mapping features. Finally, we input the features of SDAE network mapping into SVR, whose parameters are optimized by BA for prediction, so as to obtain the SDAE-SVR-BA model. In this paper, we used BA during the training process to optimize the number of hidden layers and hidden layer nodes of SDAE, the penalty factor parameter C and the kernel function radius g of the SVR model. Additionally, we verified the model with a wind farm example and compared it to the traditional model. Based on the verification data applied in this article, in a forecast for the next twelve hours, the normalized root means square error (NRMSE) of SDAE-SVR was 11.97% and the NRMSE of SDAE-SVR-BA model was 11.54%, reduced by 1.24% compared with SDAE, which demonstrates the effectiveness of the proposed method. |
| first_indexed | 2024-04-11T07:49:24Z |
| format | Article |
| id | doaj.art-586005aa63424fbcb4d401df2e9c8da3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:49:24Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-586005aa63424fbcb4d401df2e9c8da32022-12-22T04:36:08ZengIEEEIEEE Access2169-35362022-01-011012359512360410.1109/ACCESS.2022.32234359955524A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA OptimizationRuiqin Duan0Xiaosheng Peng1https://orcid.org/0000-0002-9958-7045Cong Li2https://orcid.org/0000-0002-0604-021XZimin Yang3Yan Jiang4Xiufeng Li5Shuangquan Liu6System Operation Department, Yunnan Power Grid Corporation Ltd., Kunming, 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, ChinaSystem Operation Department, Yunnan Power Grid Corporation Ltd., Kunming, ChinaSystem Operation Department, Yunnan Power Grid Corporation Ltd., Kunming, ChinaSystem Operation Department, Yunnan Power Grid Corporation Ltd., Kunming, ChinaWind power prediction (WPP) is necessary to the safe operation and economic dispatch of power systems. In order to improve the prediction accuracy of WPP, in this paper we propose a three-step model named SDAE-SVR-BA to be applied in short-term WPP based on stacked-denoising-autoencoder (SDAE) feature processing, bat algorithm (BA) optimization and support vector regression (SVR). First, we preprocessed the original NWP data input into the SDAE-SVR-BA model to adapt to the training and prediction of the proposed model. Second, we input the preprocessed features into the SDAE network, whose parameters are optimized by BA to obtain the depth-mapping features. Finally, we input the features of SDAE network mapping into SVR, whose parameters are optimized by BA for prediction, so as to obtain the SDAE-SVR-BA model. In this paper, we used BA during the training process to optimize the number of hidden layers and hidden layer nodes of SDAE, the penalty factor parameter C and the kernel function radius g of the SVR model. Additionally, we verified the model with a wind farm example and compared it to the traditional model. Based on the verification data applied in this article, in a forecast for the next twelve hours, the normalized root means square error (NRMSE) of SDAE-SVR was 11.97% and the NRMSE of SDAE-SVR-BA model was 11.54%, reduced by 1.24% compared with SDAE, which demonstrates the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/9955524/Stack denoising autoencoderbat optimization algorithmwind power prediction |
| spellingShingle | Ruiqin Duan Xiaosheng Peng Cong Li Zimin Yang Yan Jiang Xiufeng Li Shuangquan Liu A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization IEEE Access Stack denoising autoencoder bat optimization algorithm wind power prediction |
| title | A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization |
| title_full | A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization |
| title_fullStr | A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization |
| title_full_unstemmed | A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization |
| title_short | A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA Optimization |
| title_sort | hybrid three staged short term wind power prediction method based on sdae svr deep learning and ba optimization |
| topic | Stack denoising autoencoder bat optimization algorithm wind power prediction |
| url | https://ieeexplore.ieee.org/document/9955524/ |
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