Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China
Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China
复制标题
残差修正方法在季节性 ARIMA 电力需求预测中的应用：以中国为例
DOI：
10.1016/j.enpol.2012.05.026
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发表时间：
2012-09-01
期刊：
影响因子：
9
通讯作者：
Dong, Yao
中科院分区：
文献类型：
--
作者：
Wang, Yuanyuan;Wang, Jianzhou;Dong, Yao
电力需求预测可以证明是决策者的一个有用的政策工具;因此，准确的电力需求预测对于发电商和消费者制定计划都很有价值。季节性ARIMA模型在电力需求分析中得到了广泛的应用，是一种高精度的季节性数据预测方法，但在预测过程中不可避免地会出现误差。因此，进一步提高预测精度是一个重要的研究目标。为了帮助电力部门的人们做出更明智的决策，本研究提出了残差修正模型，以提高季节性ARIMA的电力需求预测精度。将粒子群优化傅立叶法、季节性ARIMA模型以及粒子群优化傅立叶法与季节性ARIMA组合模型应用于西北电网，对季节性ARIMA预测结果进行修正。修正模型预测电力需求比单一季节性ARIMA模型更具有可操作性。结果表明，三种残差修正模型的预测精度均高于单一季节性ARIMA模型，其中组合模型的预测精度最高。(C)2012爱思唯尔有限公司保留所有权利。
Electricity demand forecasting could prove to be a useful policy tool for decision-makers; thus, accurate forecasting of electricity demand is valuable in allowing both power generators and consumers to make their plans. Although a seasonal ARIMA model is widely used in electricity demand analysis and is a high-precision approach for seasonal data forecasting, errors are unavoidable in the forecasting process. Consequently, a significant research goal is to further improve forecasting precision. To help people in the electricity sectors make more sensible decisions, this study proposes residual modification models to improve the precision of seasonal ARIMA for electricity demand forecasting. In this study, PSO optimal Fourier method, seasonal ARIMA model and combined models of PSO optimal Fourier method with seasonal ARIMA are applied in the Northwest electricity grid of China to correct the forecasting results of seasonal ARIMA. The modification models forecasting of the electricity demand appears to be more workable than that of the single seasonal ARIMA. The results indicate that the prediction accuracy of the three residual modification models is higher than the single seasonal ARIMA model and that the combined model is the most satisfactory of the three models. (C) 2012 Elsevier Ltd. All rights reserved.