中国电力2025，Vol.58Issue(6)：10-18,9.DOI:10.11930/j.issn.1004-9649.202406098
基于MSCNN-BiGRU-Attention的短期电力负荷预测
Short-Term Power Load Forecasting Based on MSCNN-BiGRU-Attention
摘要
Abstract
To address the problem of difficult extraction of key features in power load,a multi-scale convolutional neural network-bi-directional gated recurrent unit-Attention(MSCNN-BiGRU-Attention)hybrid model is proposed for short-term power load forecasting.Firstly,the Spearman correlation coefficient was used to analyze the correlation of power load data set,and the features with high correlation were screened out to build the power load data set.Secondly,the data was input into the multi-scale convolutional neural network(MSCNN)to extract the multi-scale time sequence of power load data.Then,the extracted temporal features were input into the bidirectional gated recurrent unit(BiGRU)neural network for temporal prediction,and the temporal features were filtered and screened by attention mechanism.Finally,the outputs are integrated through a fully connected layer to generate the predicted values.With the 3 years of multidimensional power load data from a region in Australia as a data set,five control models were established.Meanwhile,we selected two years of multidimensional power load data from a region in southern China as the validation dataset for the models.The results show that MSCNN-BiGRU-Attention hybrid model can achieve better prediction effects than other models,thus effectively solving the problem of difficult extraction of the key features of regional power load.关键词
电力负荷预测/多尺度卷积神经网络/双向门控循环单元/注意力机制/深度学习/Spearman相关系数Key words
power load forecasting/multi-scale convolutional neural network/bi-directional gated recurrent unit/Attention mechanism/deep learning/Spearman correlation coefficient引用本文复制引用
李科,潘庭龙,许德智..基于MSCNN-BiGRU-Attention的短期电力负荷预测[J].中国电力,2025,58(6):10-18,9.基金项目
国家自然科学基金优秀青年基金资助项目(62222307) （62222307）
江苏省基础研究计划(自然科学基金)面上项目(BK20211235). This work is supported by National Natural Science Foundation of China Outstanding Youth Fund Project(No.62222307),Jiangsu Provincial Natural Science Foundation(No.BK20211235). （自然科学基金）
