A Hybrid Neural Network and ARIMA Model for Energy Consumption Forecasting

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.