Operation State Identification of Commercial & Industrial Users

Abstract

Users’ electricity usage information is helpful to promote performance of load forecasting and demand response. Users’ metering data contains abundant usage information and various approaches have been developed to extract users’ usage information from metering data. Since a user have specific several operation states and the user’s electricity consumption have particular features in each state, user’s operation state identification based approach is developed in this paper. The three phase power with an interval of 15 minutes in a day is utilized as fingerprint of the day. For the abnormal users with anomaly usage, load data should be analysed to get the load fingerprint in each day. Thereafter, the load fingerprint can be clustered with Affinity propagation algorithm. Once the user in suspicious days with much less electricity consumption has similar load fingerprint as that in holiday, the anomaly electricity consumption could be caused by alteration of operation state.