VMD-CAT: A hybrid model for short-term wind power prediction

Accurate wind power prediction is essential to optimize the wind power scheduling and maximize the profits. However, the inertia and time-varying property of the wind speed pose a challenge to the wind power prediction task. The existing prediction models fail to efficiently mitigate the negative in...

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Bibliographic Details
Published in:Energy reports 2023-06, Vol.9, p.199-211
Main Authors: Zheng, Huan, Hu, Zhenda, Wang, Xuguang, Ni, Junhong, Cui, Mengqi
Format: Article
Language:English
Subjects:
Correlation relationship
Transformer
VMD
Wind power prediction
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Summary:Accurate wind power prediction is essential to optimize the wind power scheduling and maximize the profits. However, the inertia and time-varying property of the wind speed pose a challenge to the wind power prediction task. The existing prediction models fail to efficiently mitigate the negative influence of these properties on the prediction results. Therefore, their generalization abilities require a further improvement. In this paper, the historical wind power segment is decomposed into sub-signals, which are considered as the fluctuation patterns of the wind power series, the variable support then is employed to describe the inertia and time-varying properties for the fluctuation patterns. The component-attention mechanism is introduced to formulate the correlation-relationship between each fluctuation pattern and the historical wind power segment, this mechanism is used to replace the self-attention mechanism for the Transformer model. A hybrid model combined VMD and Transformer is utilized for predicting the future wind power. Experiments performed on an actual wind power series validate the efficiency of the proposed model.
ISSN:2352-4847
2352-4847
DOI:10.1016/j.egyr.2023.02.061