摘要
近年来,风电装机规模逐年增加,风电数据采集量呈现规模化增长,面对海量多维、强波动的风电数据,风电功率预测精度仍面临一定的挑战。为提高风电功率预测精度,提出了基于卷积神经网络(convolutional neural networks,CNN)-长短期记忆网络(long short-term memory,LSTM)和梯度提升学习(light gradient boosting machine,lightGBM)组合的超短期风电功率预测方法。首先,分别建立CNN-LSTM和lightGBM的风电功率超短期预测模型。其中,CNN-LSTM模型采用CNN对风电数据集进行特征处理,并将其作为LSTM模型的数据输入,从而建立CNN-LSTM融合的预测模型;然后,采用误差倒数法对CNN-LSTM和lightGBM的预测数据进行加权组合,建立CNN-LSTM-lightGBM组合的预测模型;最后,采用张北曹碾沟风电场的风电数据集,以未来4 h风电功率为预测目标,验证了组合模型的有效性。预测结果表明:相较于其他3种单一模型,组合模型具有更高的预测精度。
In recent years,the installed scale of wind power has increased year by year,and the amount of wind power data collection has shown a large-scale growth.Facing the massive multidimensional and strongly fluctuating wind power data,the accuracy of wind power prediction still faces certain challenges.To improve the accuracy of wind power prediction,an ultra-short-term wind power prediction method based on the combination of convolutional neural networks(CNN)-long short-term memory(LSTM)and light gradient boosting machine(lightGBM)was proposed.Firstly,the ultra-short-term wind power prediction models of CNN-LSTM and lightGBM were established respectively.Among them,the CNN-LSTM model used CNN to feature the wind power dataset and used it as the data input of the LSTM model,so as to established the prediction model of CNN-LSTM fusion.Then,the error inverse method was used to combine the prediction data of CNN-LSTM and lightGBM in a weighted way to establish the combined CNN-LSTM-lightGBM prediction model.Finally,the wind power dataset of a wind farm in Zhangbei was used to verify the effectiveness of the combined model with the future 4 h wind power as the prediction target.The prediction results show that the combined model has higher prediction accuracy compared with other three single models.
作者
王愈轩
刘尔佳
黄永章
WANG Yu-xuan;LIU Er-jia;HUANG Yong-zhang(School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,China;Information and Communication Branch of State Grid Hubei Wuhan Electric Power Company,Wuhan 430000,China)
出处
《科学技术与工程》
北大核心
2022年第36期16067-16074,共8页
Science Technology and Engineering
基金
中央高校基本科研业务费专项基金(2019QN117)
国家电网公司科技项目(SGJSDK00JLXT7118041)。
