Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis

Univariate Box-Jenkins time-series analysis has been used for modeling and forecasting monthly domestic electric energy consumption in the Eastern Province of Saudi Arabia. Autoregressive integrated moving average (ARIMA) models were developed using data for 5 yr and evaluated on forecasting new data for the sixth year. The optimum model derived is a multiplicative combination of seasonal and nonseasonal autoregressive parts, each being of the first order, following first differencing at both the seasonal and nonseasonal levels. Compared to regression and abductive network machine-learning models previously developed on the same data, ARIMA models require less data, have fewer coefficients, and are more accurate. The optimum ARIMA model forecasts monthly data for the evaluation year with an average percentage error of 3.8% compared to 8.1% and 5.6% for the best multiple-series regression and abductory induction mechanism (AIM) models, respectively; the mean-square forecasting error is reduced with the ARIMA model by factors of 3.2 and 1.6, respectively. (C) 1997 Elsevier Science Ltd.