Double Seasonal ARIMA Model for Forecasting Load Demand
Double Seasonal ARIMA Model for Forecasting Load Demand
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DOI：
10.11113/matematika.v26.n.565
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发表时间：
2010-12
期刊：
影响因子：
0.8
通讯作者：
N. Mohamed;Maizah Hura Ahmad;Zuhaimy Ismail;Suhartono
中科院分区：
文献类型：
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作者：
N. Mohamed;Maizah Hura Ahmad;Zuhaimy Ismail;Suhartono
本研究探讨双季节ARIMA模型在负荷需求预测中的应用。本研究采用马来西亚2005年9月1日至2006年8月31日的一年半小时负荷需求(兆瓦)。用平均绝对百分比误差(MAPE)作为预测精度的衡量指标。采用统计分析系统、SAS统计软件对数据进行分析。利用最小二乘法估计双SARIMA模型中的系数，通过模型验证和模型选择标准，提出ARIMA(0；1；1)(0；1；1)48(0；1；1)336，样本内MAPE为0.9906%。比较k步超前预测和一步超前预测的预测效果，我们发现，超前一步的样本预测，从一周的提前期到一个月的提前期，其MAPE都小于1%。因此，我们建议将预测提前一步的双季节ARIMA模型作为预测两个季节周期的马来西亚负荷需求时间序列的最合适模型。关键词：负荷预测；双季节ARIMA模型；k步超前预测；超前一步预测。
This study investigates the use of a double seasonal ARIMA model for forecasting load demand. For the purpose of this study, a one-year half hourly Malaysia load demand from 1 September 2005 to 31 August 2006 measured in Megawatt (MW) is used. The mean absolute percentage error (MAPE) is used as the measure of forecasting accuracy. We use Statistical Analysis System, SAS package to analyze the data. Using the least squares method to estimate the coefficients in a double SARIMA model, followed by model validation and model selection criteria, we propose ARIMA(0; 1; 1)(0; 1; 1)48(0; 1; 1)336 with in-sample MAPE of 0.9906% as the best model for this study. Comparing the forecasting performances by using k-step ahead forecasts and one-step ahead forecasts, we found that the MAPE for the one-step ahead out-sample forecasts from any horizon ranging from one week lead time to one month lead time are all less than 1%. We thus propose that a double seasonal ARIMA model with one-step ahead forecast as the most appropriate model for forecasting the two-seasonal cycles Malaysia load demand time series. Keywords: Load forecasting; double seasonal ARIMA model; k-step ahead forecast; one-step ahead forecasts.