Non-Intrusive Appliances Load Monitoring System Using Neural Networks

K. Yoshimoto and Y. Nakano, Central Research Institute of Electric Power Industry
Y. Amano and B. Kermanshahi, Tokyo University of Agriculture & Technology

Keywords

Abstract

A non-intrusive appliances load monitoring system has been developed to ascertain the behavior of each electrical appliance in a household by disaggregating the total household load demand. This system does not need to intrude into a house when metering power consumption of each appliance. Therefore, the system has significant cost advantages and is less troublesome to the customers. This paper describes a non-intrusive monitoring system which is especially useful for inverter-driven appliances that frequently change their operational state. Conventional non-intrusive load monitoring systems cannot treat these inverter-driven appliances easily because of their complicated operation. In the developed system, the load consumption of household appliances is identified by the pattern recognition ability of a Neural Network (NN), which perceives the pattern of harmonics flowing out of the house. The patterns of current and phase at each harmonic order depend on what kind of appliances are in on or off. An air conditioner, a refrigerator, an incandescent lamp, a fluorescent lamp and a television set were used as household appliances to obtain training data for NN’s learning. The inference ability of the NN from unknown data was simulated. From the simulation result, it was verified that the NN could identify the operating status and load consumption of an inverter-driven air conditioner from the pattern of harmonics of the total household load. For this reason, the non-intrusive appliances load monitoring system for inverter-driven appliances such as air conditioners is viewed as reliable.