¹Department of Economy and Management, North China Electric Power University, Baoding 071003, China
²Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China

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Abstract—Energy consumption time series consists of complex linear and non-linear patterns and are difficult to forecast. Neither autoregressive integrated moving average (ARIMA) nor artificial neural networks (ANNs) can be adequate in modeling and predicting energy consumption. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linear and nonlinear patterns equally well. In the present study, a hybrid methodology that combines both ARIMA and ANN models is proposed to take advantage of the unique strength of ARIMA and ANN models in linear and nonlinear modeling. The empirical results with energy consumption data of Hebei province in China indicate that the hybrid model can be an effective way to improve the energy consumption forecasting accuracy obtained by either of the models used separately.

Index Terms—Artificial neural networks, ARIMA model, hybrid model, energy consumption, time series, forecasting.

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Cite: Xiping Wang, Ming Meng, "A Hybrid Neural Network and ARIMA Model for Energy Consumption Forecasting," Journal of Computers vol. 7, no. 5, pp. 1184-1190, 2012.