Electricity consumption forecasting using Nonlinear Autoregressive with External (Exogeneous) input neural network

K. G., Tay and Muwafaq, Hassan and Ismail, Shuhaida and Ong, Pauline (2019) Electricity consumption forecasting using Nonlinear Autoregressive with External (Exogeneous) input neural network. Universal Journal of Electrical and Electronic Engineering, 6 (5B). pp. 26-36.

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Abstract

Forecasting is prediction of future values based on historical data. Electricity consumption forecasting is crucial for utility company to plan for future power system generation. Even though there are previous works of electricity consumption forecasting using Artificial Neural Network (ANN), but most of their data is multivariate data. In this study, we have only univariate data of electricity consumption from January 2009 to December 2018 and wish to do a prediction for a year ahead. On top of that, our data consist of autoregressive component, hence Nonlinear Autoregressive with External (Exogeneous) Input (NARX) Neural Network Time Series from Matlab R2018b was used. It gives the mean absolute percentage error (MAPE) between actual and predicted electricity consumption of 1.38%.

Item Type:	Article
Uncontrolled Keywords:	ANN; NARX; electricity consumption; forecasting.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK3001-3521 Distribution or transmission of electric power
Divisions:	Faculty of Applied Science and Technology > Department of Physics and Chemistry
Depositing User:	Mr. Abdul Rahim Mat Radzuan
Date Deposited:	11 Aug 2021 08:20
Last Modified:	11 Aug 2021 08:20
URI:	http://eprints.uthm.edu.my/id/eprint/642

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