Short-Term Wind Power Prediction Based on a Variational Mode Decomposition–BiTCN–Psformer Hybrid Model

Wu Xu, Wenjing Dai, Dongyang Li and Qingchang Wu ()
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Wu Xu: School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, China
Wenjing Dai: School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, China
Dongyang Li: School of Electrical and Information Engineering, Yunnan Minzu University, Kunming 650504, China
Qingchang Wu: Lancang-Mekong International Vocational Institute, Yunnan Minzu University, Kunming 650504, China

Energies, 2024, vol. 17, issue 16, 1-17

Abstract: Precise wind power forecasting is essential for the successful integration of wind power into the power grid and for mitigating the potential effects of wind power on the power system. To enhance the precision of predictions, a hybrid VMD-BiTCN-Psformer model was devised. Firstly, VMD divided the original sequence into several data components with varying time scales. Furthermore, the BiTCN network was utilized to extract the sequence features. These features, along with the climate features, were then input into the positional encoding and ProbSparse self-attention improved Transformer model. The outputs of these models were combined to obtain the ultimate wind power prediction results. For the prediction of the wind power in Fujian Province on April 26, four additional models were developed for comparison with the VMD-BiTCN-Psformer model. The VMD-BiTCN-Psformer model demonstrated the greatest level of forecast accuracy among all the models. The R 2 increased by 22.27%, 12.38%, 8.93%, and 2.59%, respectively.

Keywords: wind power forecasting; transformer; ProbSparse self-attention; VMD; BiTCN (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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