MSVMD-Informer: A Multi-Variate Multi-Scale Method to Wind Power Prediction
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 correlat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/7/1571 |
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| Summary: | 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. |
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| ISSN: | 1996-1073 |