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Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 9649682, 9 pages
Research Article

SARIMA-Orthogonal Polynomial Curve Fitting Model for Medium-Term Load Forecasting

School of Economics and Management, North China Electric Power University, Baoding 071003, China

Received 21 June 2016; Accepted 4 October 2016

Academic Editor: Paolo Renna

Copyright © 2016 Herui Cui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Seasonal component has been a key factor in time series modeling for medium-term electric load forecasting. In this paper, a seasonal-ARIMA model is developed, but the parameters of the SAR and the SMA turn out to be quite nonsignificant in most cases during the model order selection. To address this issue, the hybrid time series model based on the HP filter is utilized to extract the spectrum sequences with different frequencies and analyze interactions among various factors. Finally, an integrative forecast is made for the electricity consumption from January to November in 2014. The empirical results demonstrate that the method with HP filter could reduce the relative error caused by the interaction between the trend component and the seasonal component.