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- Title
基于 GWO-CNN-BiLSTM 的超短期风电预测.
- Authors
程杰; 陈鼎; 李春; 钟伟东; 严婷; 窦春霞
- Abstract
In the future high-permeability wind power scenarios, the study of ultra-short-term wind power prediction research is of great significance for achieving optimal operation of power systems. Therefore, an ultra-short-term wind power prediction method based on GWO-CNN-BiLSTM was proposed. Firstly, a combined model based on convolutional neural network ( CNN) and bidirectional long short term memory ( BiLSTM) was built, and then, in order to improve the accuracy of wind power prediction results, the combined model was optimized by the grey wolf optimizer ( GWO), so that the parameters of the combined model can be adapted to the historical wind power data in real time. Finally, the simulation results verify the effectiveness and superiority of the proposed method.
- Publication
Science Technology & Engineering, 2023, Vol 23, Issue 35, p15091
- ISSN
1671-1815
- Publication type
Article