Research on Wind Power Prediction Based on a Gated Transformer
Wind power, as a type of renewable energy, has received widespread attention from domestic and foreign experts. Although it has the advantages of cleanliness and low pollution, its strong randomness and volatility can bring disadvantages to the stable operation of the power grid. Accurate power pred...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/14/8350 |
| _version_ | 1827734119596949504 |
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| author | Qiyue Huang Yapeng Wang Xu Yang Sio-Kei Im |
| author_facet | Qiyue Huang Yapeng Wang Xu Yang Sio-Kei Im |
| author_sort | Qiyue Huang |
| collection | DOAJ |
| description | Wind power, as a type of renewable energy, has received widespread attention from domestic and foreign experts. Although it has the advantages of cleanliness and low pollution, its strong randomness and volatility can bring disadvantages to the stable operation of the power grid. Accurate power prediction can avoid the adverse effects of wind power, and is of great significance for power grid frequency regulation, peak shaving, and energy improvement. However, traditional wind power prediction methods can only achieve accurate predictions in the short term and perform poorly in medium- to long-term prediction tasks. To address this issue, a power prediction model based on a Gated Transformer is proposed in this paper. Firstly, it can extract features from different types of data sources, effectively capture their correlations, and achieve data fusion. Secondly, gating unit, dilated convolution unit, and multi-head attention mechanism are added to improve the Receptive field and generalization ability of the model. In addition, adding a decoder to guide data prediction further improves the accuracy of prediction. Finally, experiments are carried out with the data collected from typical wind farms. The results show that the proposed Gated Transformer achieves consistent state-of-the-art results in prediction tasks on different time scales. |
| first_indexed | 2024-03-11T01:20:00Z |
| format | Article |
| id | doaj.art-ce4927ab6d6c4b82be75899235706274 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:20:00Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ce4927ab6d6c4b82be758992357062742023-11-18T18:11:50ZengMDPI AGApplied Sciences2076-34172023-07-011314835010.3390/app13148350Research on Wind Power Prediction Based on a Gated TransformerQiyue Huang0Yapeng Wang1Xu Yang2Sio-Kei Im3Faculty of Applied Sciences, Macao Polytechnic University, Macao, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao, ChinaFaculty of Applied Sciences, Macao Polytechnic University, Macao, ChinaMacao Polytechnic Institute, Macao, ChinaWind power, as a type of renewable energy, has received widespread attention from domestic and foreign experts. Although it has the advantages of cleanliness and low pollution, its strong randomness and volatility can bring disadvantages to the stable operation of the power grid. Accurate power prediction can avoid the adverse effects of wind power, and is of great significance for power grid frequency regulation, peak shaving, and energy improvement. However, traditional wind power prediction methods can only achieve accurate predictions in the short term and perform poorly in medium- to long-term prediction tasks. To address this issue, a power prediction model based on a Gated Transformer is proposed in this paper. Firstly, it can extract features from different types of data sources, effectively capture their correlations, and achieve data fusion. Secondly, gating unit, dilated convolution unit, and multi-head attention mechanism are added to improve the Receptive field and generalization ability of the model. In addition, adding a decoder to guide data prediction further improves the accuracy of prediction. Finally, experiments are carried out with the data collected from typical wind farms. The results show that the proposed Gated Transformer achieves consistent state-of-the-art results in prediction tasks on different time scales.https://www.mdpi.com/2076-3417/13/14/8350transformergated unitpower predictionNWPattention |
| spellingShingle | Qiyue Huang Yapeng Wang Xu Yang Sio-Kei Im Research on Wind Power Prediction Based on a Gated Transformer Applied Sciences transformer gated unit power prediction NWP attention |
| title | Research on Wind Power Prediction Based on a Gated Transformer |
| title_full | Research on Wind Power Prediction Based on a Gated Transformer |
| title_fullStr | Research on Wind Power Prediction Based on a Gated Transformer |
| title_full_unstemmed | Research on Wind Power Prediction Based on a Gated Transformer |
| title_short | Research on Wind Power Prediction Based on a Gated Transformer |
| title_sort | research on wind power prediction based on a gated transformer |
| topic | transformer gated unit power prediction NWP attention |
| url | https://www.mdpi.com/2076-3417/13/14/8350 |
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