Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model
When using the Transformer model for wind power prediction, the accuracy of the model predictions tends to be reduced due to the shift in the wind power data distribution, channel mixing, and the inability of the model to establish strong correlations. To address these challenges, this paper propose...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858711/ |
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| author | Yanlong Gao Feng Xing Lipeng Kang Mingming Zhang Caiyan Qin |
| author_facet | Yanlong Gao Feng Xing Lipeng Kang Mingming Zhang Caiyan Qin |
| author_sort | Yanlong Gao |
| collection | DOAJ |
| description | When using the Transformer model for wind power prediction, the accuracy of the model predictions tends to be reduced due to the shift in the wind power data distribution, channel mixing, and the inability of the model to establish strong correlations. To address these challenges, this paper proposes an ultra-short-term wind power prediction model based on the DT-DSCTransformer. First, the model applies DT’s self-learning standardization and de-standardization parameters to standardize the input and de-standardize the output, mitigating the impact forecasting of data distribution shifts on prediction accuracy. Second, the proposed De-Stationary Channel Attention (DSCAttention) mechanism is introduced. By incorporating De-Stationary Attention (DSAttention) into the channel attention mechanism while maintaining channel independence, the model establishes stronger inter-channel correlations, addressing the performance degradation caused by channel mixing and weak correlations. Finally, experimental analysis demonstrates that the proposed model achieves the highest prediction accuracy compared to commonly used time series forecasting models. |
| format | Article |
| id | doaj-art-19b7fe082c6644d49502d047d3b920b0 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-19b7fe082c6644d49502d047d3b920b02025-02-07T00:01:12ZengIEEEIEEE Access2169-35362025-01-0113229192293010.1109/ACCESS.2025.353715810858711Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer ModelYanlong Gao0https://orcid.org/0009-0009-7880-976XFeng Xing1https://orcid.org/0000-0002-9741-4132Lipeng Kang2https://orcid.org/0009-0006-0903-6490Mingming Zhang3https://orcid.org/0009-0004-4299-9243Caiyan Qin4https://orcid.org/0000-0002-2487-500XSchool of Electrical Engineering, Liaoning University of Technology, Jinzhou, ChinaSchool of Electrical Engineering, Liaoning University of Technology, Jinzhou, ChinaSchool of Electrical Engineering, Liaoning University of Technology, Jinzhou, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, ChinaSchool of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, ChinaWhen using the Transformer model for wind power prediction, the accuracy of the model predictions tends to be reduced due to the shift in the wind power data distribution, channel mixing, and the inability of the model to establish strong correlations. To address these challenges, this paper proposes an ultra-short-term wind power prediction model based on the DT-DSCTransformer. First, the model applies DT’s self-learning standardization and de-standardization parameters to standardize the input and de-standardize the output, mitigating the impact forecasting of data distribution shifts on prediction accuracy. Second, the proposed De-Stationary Channel Attention (DSCAttention) mechanism is introduced. By incorporating De-Stationary Attention (DSAttention) into the channel attention mechanism while maintaining channel independence, the model establishes stronger inter-channel correlations, addressing the performance degradation caused by channel mixing and weak correlations. Finally, experimental analysis demonstrates that the proposed model achieves the highest prediction accuracy compared to commonly used time series forecasting models.https://ieeexplore.ieee.org/document/10858711/Transformerwind power predictiondistribution shiftDTDSCAttention |
| spellingShingle | Yanlong Gao Feng Xing Lipeng Kang Mingming Zhang Caiyan Qin Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model IEEE Access Transformer wind power prediction distribution shift DT DSCAttention |
| title | Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model |
| title_full | Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model |
| title_fullStr | Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model |
| title_full_unstemmed | Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model |
| title_short | Ultra-Short-Term Wind Power Forecasting Based on DT-DSCTransformer Model |
| title_sort | ultra short term wind power forecasting based on dt dsctransformer model |
| topic | Transformer wind power prediction distribution shift DT DSCAttention |
| url | https://ieeexplore.ieee.org/document/10858711/ |
| work_keys_str_mv | AT yanlonggao ultrashorttermwindpowerforecastingbasedondtdsctransformermodel AT fengxing ultrashorttermwindpowerforecastingbasedondtdsctransformermodel AT lipengkang ultrashorttermwindpowerforecastingbasedondtdsctransformermodel AT mingmingzhang ultrashorttermwindpowerforecastingbasedondtdsctransformermodel AT caiyanqin ultrashorttermwindpowerforecastingbasedondtdsctransformermodel |