ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON PM-iTransformer-TCN AND BAYESIAN OPTIMIZATION

Zhou Jiaxuan; Gu Xiwen; Mei Yiming; Yang Shixi; Shan Zongjun

doi:10.19912/j.0254-0096.tynxb.2025-0773

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 805-812. DOI: 10.19912/j.0254-0096.tynxb.2025-0773

ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON PM-iTransformer-TCN AND BAYESIAN OPTIMIZATION

Zhou Jiaxuan¹, Gu Xiwen¹, Mei Yiming², Yang Shixi¹, Shan Zongjun³

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Abstract

A multi-step ultra-short-term wind power forecasting method based on PM-iTransformer-TCN and Bayesian optimization （BO） is proposed to address the limitations of traditional machine learning methods, which struggle with the strong fluctuations of wind power and demonstrate insufficient prediction accuracy. First, the original wind power data is preprocessed using the Perona-Malik algorithm to suppress random components. Next, the iTransformer model combines multiple input variables, including wind speed, wind direction, temperature, and air pressure, to generate preliminary predicted values of wind power. Finally, real-time tracking and correction of multi-step prediction errors are achieved using Temporal Convolutional Networks （TCNs）, tailored to the fluctuation characteristics of wind power data. BO is applied in the training process to select the optimal parameter combination. The proposed method is compared with several baseline machine learning models using on-site data from a wind farm in Gansu Province, China. The results indicate that the normalized root mean square errors of the forecasting results at future 2 h and 4 h are 18.95% and 21.64%, respectively, outperforming traditional machine learning methods and significantly enhancing the accuracy of ultra-short-term wind power forecasting.

Key words

wind power / forecasting / wind farm / Perona-Malik algorithm / iTransformer model / temporal convolutional network / Bayesian optimization

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Zhou Jiaxuan, Gu Xiwen, Mei Yiming, Yang Shixi, Shan Zongjun. ULTRA-SHORT-TERM WIND POWER FORECASTING BASED ON PM-iTransformer-TCN AND BAYESIAN OPTIMIZATION[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 805-812 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0773

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