Evaluation of Moving Average Model and Autoregressive Moving Average Model (ARMA) for Prediction of Industrial Electricity Consumption in Nigeria
American Journal of Software Engineering and Applications
Volume 6, Issue 3, June 2017, Pages: 67-73
Received: Jan. 29, 2017; Accepted: Mar. 30, 2017; Published: Jun. 12, 2017
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Idorenyin Markson, Department of Mechanical Engineering, University of Uyo, Uyo, Nigeria
Mfonobong Charles Uko, Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
Aneke Chikezie, Department of Electrical/Electronic and Computer Engineering, University of Uyo, Uyo, Nigeria
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In this paper, evaluation of moving average model and autoregressive moving average model (ARMA) for prediction of industrial electricity consumption in Nigeria is presented. Industrial electricity consumption data obtained from Central Bank of Nigeria (CBN) Statistical Bulletin for the year 1979-2014 is used to determine the model parameters and prediction performance in terms of Root Mean Square Error (RMSE) and Coefficient of determination r2 values. The results show that the Autoregressive Moving Average (ARMA) model with coefficient of determination value of 66.0% and RMSE value of 68.628 gives better prediction performance than the Moving Average with coefficient of determination value of 42.6% and value of 84.749. However, coefficient of determination value of 66% is not particularly adequate for acceptable prediction accuracy. In that case, for better prediction accuracy for the industrial electricity consumption in Nigeria, other models may need to be examined apart from the two models considered in this paper.
Moving Average Model, Autoregressive Moving Average Model, Industrial Electricity Consumption, Prediction Accuracy, Time Series Models
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Idorenyin Markson, Mfonobong Charles Uko, Aneke Chikezie, Evaluation of Moving Average Model and Autoregressive Moving Average Model (ARMA) for Prediction of Industrial Electricity Consumption in Nigeria, American Journal of Software Engineering and Applications. Vol. 6, No. 3, 2017, pp. 67-73. doi: 10.11648/j.ajsea.20170603.12
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