电力系统自动化2019，Vol.43Issue(1)：126-132,167,8.DOI:10.7500/AEPS20180629004
深度神经网络在非侵入式负荷分解中的应用
Application of Deep Neural Network in Non-intrusive Load Disaggregation
摘要
Abstract
Load monitoring is an important part of intelligent electricity consumption.For the non-intrusive load monitoring, a deep neural network based non-intrusive load disaggregation method is proposed.Firstly, a modified iterative appliance state clustering algorithm is proposed.By modifying the stopping criteria and adding eliminating criteria of redundant clusters, the clustering results are more consistent with the actual appliance operation.An appliance time characteristic model is proposed considering weak time characteristics of hidden Markov models which are commonly used in the current study.The appliance characteristics and user habits are taken into consideration.The electrical appliances are modeled from the perspective of time.A deep neural network is constructed to perform load disaggregation.The input of the network includes appliance states, power and time information.The history data and the generated data based on models are used to train the network parameters.The effectiveness and accuracy of the method are verified on the data set.关键词
非侵入式负荷监测/电器状态聚类/时间特性模型/深度神经网络Key words
non-intrusive load monitoring/appliance state clustering/time characteristic model/deep neural network引用本文复制引用
燕续峰,翟少鹏,王治华,王芬,何光宇..深度神经网络在非侵入式负荷分解中的应用[J].电力系统自动化,2019,43(1):126-132,167,8.基金项目
国家自然科学基金资助项目(51877134) This work is supported by National Natural Science Foundation of China (No. 51877134). （51877134）
