MSVMD-Informer: A Multi-Variate Multi-Scale Method to Wind Power Prediction

Zhijian Liu, Jikai Chen (), Hang Dong and Zizhuo Wang
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Zhijian Liu: Faculty of Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
Jikai Chen: Faculty of Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
Hang Dong: Faculty of Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China
Zizhuo Wang: Faculty of Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China

Energies, 2025, vol. 18, issue 7, 1-18

Abstract: Wind power prediction plays a crucial role in enhancing power grid stability and wind energy utilization efficiency. Existing prediction methods demonstrate insufficient integration of multi-variate features, such as wind speed, temperature, and humidity, along with inadequate extraction of correlations between variables. This paper proposes a novel multi-variate multi-scale wind power prediction method named multi-scale variational mode decomposition informer (MSVMD-Informer). First, a multi-scale modal decomposition module is designed to decompose univariate time-series features into multiple scales. Adaptive graph convolution is applied to extract correlations between scales, while self-attention mechanisms are utilized to capture temporal dependencies within the same scale. Subsequently, a multi-variate feature fusion module is proposed to better account for inter-variable correlations. Finally, the informer is reconstructed by integrating the aforementioned modules, enabling multi-variate multi-scale wind power forecasting. The proposed method was evaluated through comparative experiments and ablation studies against seven baselines using a public dataset and two private datasets. Experimental results demonstrate that our proposed method achieves optimal metric performance, with its lowest MAPE scores being 1.325%, 1.500% and 1.450%, respectively.

Keywords: multi-variate time series; wind power prediction; multi-scale decomposition; informer; self-attention mechanism (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: 2025
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