风力发电
风速
惯性
风电预测
计算机科学
变压器
电力系统
控制理论(社会学)
功率(物理)
工程类
人工智能
气象学
电压
电气工程
物理
控制(管理)
经典力学
量子力学
作者
Huan Zheng,Zhenda Hu,Xuguang Wang,Jingchen Ni,Mengqi Cui
标识
DOI:10.1016/j.egyr.2023.02.061
摘要
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.
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