Ultra-Short-Term Wind Power Prediction Based on the ZS-DT-PatchTST Combined Model

Yanlong Gao, Feng Xing, Lipeng Kang, Mingming Zhang and Caiyan Qin ()
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Yanlong Gao: School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China
Feng Xing: School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China
Lipeng Kang: School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China
Mingming Zhang: School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China
Caiyan Qin: School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China

Energies, 2024, vol. 17, issue 17, 1-21

Abstract: When using point-by-point data input with former series models for wind power prediction, the prediction accuracy decreases due to data distribution shifts and the inability to extract local information. To address these issues, this paper proposes an ultra-short-term wind power prediction model based on the Z-score (ZS), Dish-TS (DT), and Patch time series Transformer (PatchTST). Firstly, to reduce the impact of data distribution shift on prediction accuracy, ZS standardization is applied to both training and testing datasets. Additionally, the DT algorithm, which can self-learn the mean and variance, is introduced for window data standardization. Secondly, the PatchTST model is employed to convert point input data into local-level input data. Feature extraction is then performed using the multi-head attention mechanism in the Encoder layer and a feed-forward network composed of one-dimensional convolution to obtain the prediction results. These results are subsequently de-standardized using DT and ZS to restore the original data amplitude. Finally, experimental analysis is conducted, comparing the proposed ZS-DT-PatchTST model with various prediction models. The proposed model achieves the highest prediction accuracy, with a mean absolute error of 5.95 MW, a mean squared error of 10.89 MW, and a coefficient of determination of 97.38%.

Keywords: wind power prediction; distribution shift; Z-score; Dish-TS; PatchTST (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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