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

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Article ID: iaor20124474
Volume: 48
Issue: 2
Start Page Number: 284
End Page Number: 294
Publication Date: Sep 2012
Journal: Energy Policy
Authors: , , ,
Keywords: demand, forecasting: applications, decision
Abstract:

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.

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