Automatic recognition of electric loads analyzing the characteristic parameters of the consumed electric power through a Non-Intrusive Monitoring methodology

Abstract

Non-Intrusive Load Monitoring (NILM) consists in measuring the electricity consumption using a power consumption data acquisition system, typically placed in the main supply of the building. Relying on a single point of measure it is also called one-sensor metering in contrast to the common metering hardware that can be embedded in each appliance (electronic metering ore-metering) and in differentiation with the common utility smart meters. NILM is the process in which you are able to disaggregate a set of energy readings over a period of time to determine exactly what appliances have used the power and how much power each appliance has used during that time period. In this work a supervised classification method was employed for offline appliances classification, based on low frequency power consumption. The classification feature set consists of the true power, reactive power, and the step changing of the true power. Multiple classifiers were tested and evaluated, such as Decision Tree, Nearest Neighbor, Discriminant Analysis, and the multilayer Feed-forward Neural Network classifier. The methods were tested on the ACS-F2 appliance consumption signatures database.