Electricity Theft Prediction on Low Voltage Distribution System Using Autoregressive

Abstract

Electricity consumers tend to avoid the payment of electricity dues through various methods such as tampering with energy meter and illegal tapping via direct connection to the distribution feeder. This has led to huge revenue losses by the electricity supplying corporation and the related government or private agencies. A new approach of detecting electricity theft on low voltage distribution systems, either single or three phase, based on the advanced signal processing using linear prediction is presented in this paper. Consumer data were analyzed using Autoregressive (AR) model in order to predict the quantity of power consumed within the specified interval and consequently, compare the result obtained with the actual data recorded against the consumer under study. Thus the model developed was used to predict power consumption at 30minutes interval ahead, thereby facilitating the detection of electricity theft if there is a wide variation between the actual and the predicted data.