吉林大学学报（信息科学版）2023，Vol.41Issue(6)：1007-1014,8.
基于VMD的CNN-BiLSTM-Att的短期负荷预测
Short-Term Load Prediction of CNN-BiLSTM-Att Based on VMD
摘要
Abstract
In order to improve the accuracy of short-term power load prediction,a CNN-BiLSTM-Att(Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention)short-term load prediction model based on variational mode decomposition VMD(Variational Mode Decomposition)is proposed.In this model,the historical load data is decomposed into multiple sub-sequence loads using VMD and combined with weather,date,type of working day and other factors as input characteristics.The predicted value of each sub-sequence load is predicted by this model,and then added and reconstructed to form the actual load prediction curve.By comparison with other models,the VMD-CNN-BiLSTM-Att model has a decrease in the test set.In the continuous weekly load prediction,the average absolute percentage error of daily load prediction is basically maintained between 1％～2％.In the non-working days with complex load changes,the mean absolute percentage error is reduced by 0.13％compared with the CNN-LSTM model.It is proved that VMD-CNN-BiLSTM-Att short-term load forecasting model can improve the accuracy of power load forecasting.关键词
变分模态分解/卷积网络/长短期记忆网络/注意力机制/短期负荷预测Key words
variational mode decomposition(VMD)/convolutional network/long and short term memory network/attention mechanism/short-term load forecasting分类
信息技术与安全科学引用本文复制引用
王金玉,胡喜乐,闫冠宇..基于VMD的CNN-BiLSTM-Att的短期负荷预测[J].吉林大学学报（信息科学版）,2023,41(6):1007-1014,8.基金项目
海南省重点研发基金资助项目(ZDYF2022GXJS003) （ZDYF2022GXJS003）
