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Short-term prediction of wind power based on TCN and the informer model
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  • Shuohe Wang,
  • Linhua Chang,
  • Han Liu,
  • Yujian Chang,
  • Qiang Xue
Shuohe Wang
Shijiazhuang Tiedao University

Corresponding Author:[email protected]

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Linhua Chang
Shijiazhuang Tiedao University
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Han Liu
Tianjin Municipal Engineering Design and Research Institute
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Yujian Chang
Shijiazhuang Tiedao University
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Qiang Xue
Shijiazhuang Tiedao University
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In this study, a new short-term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short-term prediction. First, an input feature selection method based on the maximum information coefficient is proposed after considering the problem of information interference caused by excessively large input features. A dynamic time planning method is used to select the optimal input step of historical power. Then, the combined forecasting model composed of TCN and the Informer is constructed in accordance with the numerical weather forecast and historical power data. Lastly, the pinball loss function is used to expand the prediction model into a quantile regression model, measure the effect of volatility, quantify the volatility range of prediction, and finally, obtain a deterministic prediction result. The actual measured data of wind farms in the Bohai Sea area are selected for analysis and calculation. Results show that the prediction model proposed in this study achieves better accuracy in deterministic prediction and interval prediction than the traditional model.
25 Jun 2023Submitted to IET Generation, Transmission & Distribution
26 Jun 2023Submission Checks Completed
26 Jun 2023Assigned to Editor
30 Jun 2023Reviewer(s) Assigned
17 Jul 2023Review(s) Completed, Editorial Evaluation Pending
22 Jul 2023Editorial Decision: Revise Major
17 Aug 20231st Revision Received
21 Aug 2023Assigned to Editor
21 Aug 2023Submission Checks Completed
21 Aug 2023Review(s) Completed, Editorial Evaluation Pending
23 Aug 2023Reviewer(s) Assigned
28 Oct 2023Editorial Decision: Revise Minor
10 Nov 20232nd Revision Received
15 Nov 2023Submission Checks Completed
15 Nov 2023Assigned to Editor
15 Nov 2023Review(s) Completed, Editorial Evaluation Pending
15 Nov 2023Reviewer(s) Assigned
20 Nov 2023Editorial Decision: Accept