科技创新与应用2024，Vol.14Issue(7)：35-40,6.DOI:10.19981/j.CN23-1581/G3.2024.07.009
基于EWT-CNN-BiGRU的多特征电力负荷预测模型
摘要
Abstract
In order to solve the problem of insufficient accuracy of multi-feature forecasting models in short-term power load data,a convolutional neural network(CNN)fusion bi-directional gated cycle unit(BiGRU)forecasting model based on empirical wavelet transform(EWT)is proposed.Firstly,strong correlation features are extracted from multi-dimensional time series data,and then the selected features are transformed by empirical wavelet transform,and the time series data are mapped to the frequency domain to obtain sub-sequences.the prediction of power load data is realized by convolution neural network and bi-directional gated cycle unit fusion model.The prediction model is verified by experiments using the time series data of a combined cycle power plant in Germany.The results show that the prediction model has a goodness of fit of 99.463%and has a good prediction effect.关键词
电力负荷预测/经验小波变换/卷积神经网络/双向门控循环单元/预测模型Key words
power load forecasting/empirical wavelet transform(EWT)/convolutional neural network(CNN)/bi-directional gated cycle unit(BiGRU)/forecasting model分类
信息技术与安全科学引用本文复制引用
保富,孙梦觉,邓安明,周植高..基于EWT-CNN-BiGRU的多特征电力负荷预测模型[J].科技创新与应用,2024,14(7):35-40,6.基金项目
云南电网有限责任公司信息中心研发基金(059300202021030302YY00012) （059300202021030302YY00012）
