微型电脑应用2026，Vol.42Issue(4)：105-110,6.
基于混合深度学习模型的电力用户用电模式识别研究
Research on Power User Electricity Consumption Pattern Recognition Based on Hybrid Deep Learning Models
摘要
Abstract
Aiming at the problem of low performance for electricity consumption pattern recognition of power users,a model for electricity consumption pattern recognition of power users based on hybrid deep learning model is proposed.One-dimensional time series data is preprocessed and transformed into two-dimensional matrix data,thus improving the training performance.A hybrid deep learning model is established.The convolutional neural network(CNN)is used to extract spatial features,the long and short-term memory network(LSTM)is used to extract time dimension features,and the random forest(RF)is used to de-code and output power consumption categories.The feature correlation based on priority is proposed to solve the problem of low recognition performance when there are low frequency or inconspicuous features in the electricity consumption data.The simula-tion results show that compared with RF,CNN and LSTM,the proposed model has better accuracy(ACC)and Matthews cor-relation coefficient(MCC)index.The simulation results verify the generalization ability and practicability of the proposed mod-el.关键词
电力数据/模式识别/卷积神经网络/长短时记忆/随机森林算法Key words
electricity data/pattern recognition/convolutional neural network/long and short-term memory/random forest al-gorithm分类
信息技术与安全科学引用本文复制引用
郭亮,王楚雄,王琮辉..基于混合深度学习模型的电力用户用电模式识别研究[J].微型电脑应用,2026,42(4):105-110,6.基金项目
中国南方电网有限责任公司科技项目(GDKJXM20220910) （GDKJXM20220910）
