Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis
Forecasting monthly electric energy consumption in eastern Saudi Arabia using univariate time-series analysis
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DOI：
10.1016/s0360-5442(97)00032-7
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发表时间：
1997-11-01
期刊：
影响因子：
9
通讯作者：
AlGarni, AZ
中科院分区：
文献类型：
--
作者：
AbdelAal, RE;AlGarni, AZ
单变量Box-Jenkins时间序列分析已被用于建模和预测沙特阿拉伯东部省的每月家庭电力消耗。利用5年的数据开发了自回归综合移动平均（ARIMA）模型，并对预测第六年的新数据进行了评估。得到的最优模型是季节性和非季节性自回归部分的乘法组合，每个部分都是一阶的，在季节性和非季节性水平上遵循一阶差分。与之前在相同数据上开发的回归和溯因网络机器学习模型相比，ARIMA模型需要更少的数据，具有更少的系数，并且更准确。最佳ARIMA模型预测评价年的月度数据的平均百分比误差为3.8%，而最佳多序列回归模型和外展诱导机制（AIM）模型的平均百分比误差分别为8.1%和5.6%；ARIMA模型的均方预测误差分别降低了3.2和1.6个因子。1997爱思唯尔科学有限公司
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.