VMD-CAT: A hybrid model for short-term wind power prediction - Huan Zheng; Zhenda Hu; Xuguang Wang; Junhong Ni; Mengqi Cui (Energy Reports, 2023)
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.