A study on the feasibility of automated data labeling and training using an EMF sensor in NILM platforms


Non-Intrusive Load Monitoring (NILM) has been studied for a few decades now as a method of disaggregating information about appliance level power consumption in a building from measurements obtained at a centralized location in the electrical system. The training phase required at the beginning of a NILM setup is a big hindrance to wide adoption of the technique. One of the recent advances in this research area includes the addition of an Electro-Magnetic Field (EMF) sensor that measures the electric and magnetic field around an appliance to detect its state. This information, when coupled with the aggregate power data, can effectively train a NILM system almost automatically, which is a significant step towards automating the training phase. This paper explores the theory behind the operation of the EMF sensor and analyzes the feasibility in terms of automating the training and classification process. It then outlines our plan for further analysis.

Proceedings of the 2012 International EG-ICE Workshop on Intelligent Computing