Wind power prediction based on WT-BiGRU-attention-TCN model
Accurate wind power prediction is crucial for the safe and stable operation of the power grid. However, wind power generation has large random volatility and intermittency, which increases the difficulty of prediction. In order to construct an effective prediction model based on wind power generatio...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1156007/full |
| _version_ | 1827967983569338368 |
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| author | Dianwei Chi Chaozhi Yang |
| author_facet | Dianwei Chi Chaozhi Yang |
| author_sort | Dianwei Chi |
| collection | DOAJ |
| description | Accurate wind power prediction is crucial for the safe and stable operation of the power grid. However, wind power generation has large random volatility and intermittency, which increases the difficulty of prediction. In order to construct an effective prediction model based on wind power generation power and achieve stable grid dispatch after wind power is connected to the grid, a wind power generation prediction model based on WT-BiGRU-Attention-TCN is proposed. First, wavelet transform (WT) is used to reduce noises of the sample data. Then, the temporal attention mechanism is incorporated into the bi-directional gated recurrent unit (BiGRU) model to highlight the impact of key time steps on the prediction results while fully extracting the temporal features of the context. Finally, the model performance is enhanced by further extracting more high-level temporal features through a temporal convolutional neural network (TCN). The results show that our proposed model outperforms other baseline models, achieving a root mean square error of 0.066 MW, a mean absolute percentage error of 18.876%, and the coefficient of determination (R2) reaches 0.976. It indicates that the noise-reduction WT technique can significantly improve the model performance, and also shows that using the temporal attention mechanism and TCN can further improve the prediction accuracy. |
| first_indexed | 2024-04-09T18:14:11Z |
| format | Article |
| id | doaj.art-1a700ca21c614a06a232eaaeb6907568 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-09T18:14:11Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-1a700ca21c614a06a232eaaeb69075682023-04-13T05:07:46ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-04-011110.3389/fenrg.2023.11560071156007Wind power prediction based on WT-BiGRU-attention-TCN modelDianwei Chi0Chaozhi Yang1School of Artificial Intelligence, Yantai Institute of Technology, Yantai, ChinaCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao, ChinaAccurate wind power prediction is crucial for the safe and stable operation of the power grid. However, wind power generation has large random volatility and intermittency, which increases the difficulty of prediction. In order to construct an effective prediction model based on wind power generation power and achieve stable grid dispatch after wind power is connected to the grid, a wind power generation prediction model based on WT-BiGRU-Attention-TCN is proposed. First, wavelet transform (WT) is used to reduce noises of the sample data. Then, the temporal attention mechanism is incorporated into the bi-directional gated recurrent unit (BiGRU) model to highlight the impact of key time steps on the prediction results while fully extracting the temporal features of the context. Finally, the model performance is enhanced by further extracting more high-level temporal features through a temporal convolutional neural network (TCN). The results show that our proposed model outperforms other baseline models, achieving a root mean square error of 0.066 MW, a mean absolute percentage error of 18.876%, and the coefficient of determination (R2) reaches 0.976. It indicates that the noise-reduction WT technique can significantly improve the model performance, and also shows that using the temporal attention mechanism and TCN can further improve the prediction accuracy.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1156007/fullpower gridwind powerwavelet transformgated recurrent unitattention mechanismtemporal convolutional neural network |
| spellingShingle | Dianwei Chi Chaozhi Yang Wind power prediction based on WT-BiGRU-attention-TCN model Frontiers in Energy Research power grid wind power wavelet transform gated recurrent unit attention mechanism temporal convolutional neural network |
| title | Wind power prediction based on WT-BiGRU-attention-TCN model |
| title_full | Wind power prediction based on WT-BiGRU-attention-TCN model |
| title_fullStr | Wind power prediction based on WT-BiGRU-attention-TCN model |
| title_full_unstemmed | Wind power prediction based on WT-BiGRU-attention-TCN model |
| title_short | Wind power prediction based on WT-BiGRU-attention-TCN model |
| title_sort | wind power prediction based on wt bigru attention tcn model |
| topic | power grid wind power wavelet transform gated recurrent unit attention mechanism temporal convolutional neural network |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1156007/full |
| work_keys_str_mv | AT dianweichi windpowerpredictionbasedonwtbigruattentiontcnmodel AT chaozhiyang windpowerpredictionbasedonwtbigruattentiontcnmodel |