Non-intrusive Load Monitoring Based on Regularized ResNet with Multivariate Control Chart

Oh, Cheolhwan; Jeong, Jongpil

With the development of industry and the spread of the Smart Home, the need for power monitoring solution technologies for effective energy management systems is increasing. Of these, non-intrusive load monitoring (NILM), is an efficient way to solve the electricity consumption monitoring problem. NILM is a technique to measure the power consumption of individual devices by analyzing the power data collected through smart meters and commercial devices. In this paper, we propose a deep neural network (DNN)-based NILM technique that enables energy disaggregation and power consumption monitoring simultaneously. Energy disaggregation is performed by learning a deep residual network for performing multilabel regression. Real-time monitoring is performed using a multivariate control chart technique using latent variables extracted through weights of the trained model. The energy disaggregation and monitoring performance of the proposed method is verified using the public NILM Electricity Consumption and Occupancy (ECO) data set.