电力勘测设计Issue(7)：23-28,57,7.DOI:10.13500/j.dlkcsj.issn1671-9913.2024.07.005
基于CNN-BiGRU网络的超短期风电功率预测
Convolutional Neural Networks and Bidirectional Gated Recurrent Unit Model Based Ultra-Short-Term Wind Power Prediction
万黎升 1陈凡 1傅裕 1井思桐1
作者信息
- 1. 中国电建集团江西省电力设计院有限公司,江西 南昌 330096
- 折叠
摘要
Abstract
Due to the characteristics of multi-dimensional and large fluctuation of wind power data,it is difficult to predict wind power,this paper proposes a wind power prediction model based on convolutional neural networks(CNN)and bidirectional gated recurrent unit(BiGRU).The best combination of historical power and meteorological factors is selected by Pearson correlation coefficient in this model,the CNN network is used to extract the time series features of the original data,and then the BiGRU network is used to capture the time series dependence between these features,and finally,the wind power prediction values are obtained.The analysis of calculation examples shows that the CNN-BiGRU model proposed in this paper has higher prediction accuracy than the traditional BP and BiGRU neural network models.关键词
风电功率预测/Pearson相关系数/卷积神经网络/双向门控循环单元Key words
wind power prediction/pearson correlation coefficient/convolutional neural network/bidirectional gated recurrent unit分类
动力与电气工程引用本文复制引用
万黎升,陈凡,傅裕,井思桐..基于CNN-BiGRU网络的超短期风电功率预测[J].电力勘测设计,2024,(7):23-28,57,7.
