Load identification from power recordings at meter panel in residential households

Abstract

Identification of electrical appliance usage(s) from the meter panel power reading has become an area of study on its own. Many approaches over the years have used signal processing approaches at a high sampling rate (1 second typically) to evaluate the appliance load signature and subsequently used pattern recognition techniques for identification from a previously trained classifier(s). The proposed approach tries to identify the usage of high power consuming appliance(s) by using the aggregate power consumption at 10 minutes interval from the meter panel. The novelty of the approach lies in using a time series windowing approach which gives addition information about an aggregate power state. The usage of hour of the day as input to the systems also takes into account the temporal behavior of residential users. The usage of Multi-label classification approach for identification is also new for this domain. The model is tested over the IRISE data set and the results are encouraging. Due to its low sampling rate with time stamped aggregate power at 10 minutes scale as the only input from the user, the proposed approach is both practical and affordable.