Consumer Load Prediction Based on NARX for Electricity Theft Detection
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
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.
Description
Keywords
Nonlinear autoregressive with eXogenous input, consumer load prediction, energy management, data acquisition, Electricity theft detection, Power distribution network, Bayesian backpropagation technique
Citation
Abdullateef, A. I., Salami, M. J. E., & Tijani, B. I. (2016). Consumer Load Prediction Based on NARX for Electricity Theft Detection. Proceedings - 6th International Conference on Computer and Communication Engineering: Innovative Technologies to Serve Humanity, ICCCE 2016, 294–299, Published by IEEE. Available online at https://ieeexplore.ieee.org/document/7808328