基于卷积神经网络的非侵入式负荷监测方法

从深度学习与边缘计算的角度,对适用于电力物联网的非侵入式负荷监测方法展开了研究.针对NILM系统在物联网场景下的部署问题,提出了一种新的边缘计算架构,并讨论了各组成部分的任务分配.针对负荷激活在线提取问题,提出了基于离散度和用电行为规律分析的激活判断策略;针对低频采样下的负荷特征问题,提出了一种可自动提取激活特征并识别...

A Convolutional Neural Network Based Non-Intrusive Load Monitoring Method

From the perspective of deep learning and edge computing, Electric Power IoT compatible NILM system is studied in the paper. Firstly, centered on the NILM system deployment solution in IoT scenarios, a novel edge computing framework is proposed, of which the component roles are discussed. Furthermore, aimed at online extraction of load activations, a dispersion evaluation and load behavior regularity analysis-based activation detection strategy is proposed; For low sample frequency input feature extraction, a CNN architecture that can automatically extract activation signatures and recognize the load type is proposed. Plus, by the analysis of activation background power, power value fluctuation, etc., three generic features are defined as a supplement to the CNN extracted features. Last, a verification experiment is carried out on a domestic dataset, of which the result proves the proposed algorithm''s improvement on generalization and computing efficiency.