Consumer Load Prediction Based on NARX for Electricity Theft Detection

Abstract

A range of load prediction techniques has largely been used for energy management at various levels. However, the data used for the prediction are cumulative energy data, which reveal the activities of consumers and not individual consumer, on the distribution power network. Individual consumer data is essential for real time prediction, monitoring and detect of electricity theft. A new approach of monitoring individual consumer based on consumer load prediction using nonlinear autoregressive with eXogenous input (NARX) network is considered in this study. One month average energy consumption data acquired from consumer load prototype developed was used. Consequently, 5-minute step ahead load prediction was achieved. The NARX architecture was based on nine hidden neurons and two tapped delay and the network trained using Bayesian regulation backpropagation technique. The data set contains a total of 8928 data points representing energy consumed at five minute interval for one month. The data was divided into two sets at ratio 70:30 for training and validation, respectively. The training data equals 6206 while the validation data is 2722. MATLAB environment was used for the processing of the data. The training and validation MSE is 0.0225 and 0.0533 respectively, while the total time for the training is 0.016s.