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Journal of Applied Mathematics
Volume 2014, Article ID 243171, 7 pages
Research Article

Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models

1School of Economics and Management, North China Electric Power University, No. 619, Yonghua Street, Baoding, Hebei 071003, China
2Soft Science Research Base of Hebei Province, North China Electric Power University, Baoding, Hebei 071003, China
3School of Economics, Hebei University, Baoding, Hebei 071002, China

Received 24 December 2013; Accepted 5 May 2014; Published 15 May 2014

Academic Editor: Shan Zhao

Copyright © 2014 Ming Meng 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.


Monthly electric energy consumption forecasting is important for electricity production planning and electric power engineering decision making. Multiwindow moving average algorithm is proposed to decompose the monthly electric energy consumption time series into several periodic waves and a long-term approximately exponential increasing trend. Radial basis function (RBF) artificial neural network (ANN) models are used to forecast the extracted periodic waves. A novel hybrid growth model, which includes a constant term, a linear term, and an exponential term, is proposed to forecast the extracted increasing trend. The forecasting results of the monthly electric energy consumption can be obtained by adding the forecasting values of each model. To test the performance by comparison, the proposed and other three models are used to forecast China's monthly electric energy consumption from January 2011 to December 2012. Results show that the proposed model exhibited the best performance in terms of mean absolute percentage error (MAPE) and maximal absolute percentage error (MaxAPE).