Energy consumption forecasting using ARIMA and neural network models

Energy forecast is essential for a good planning of the electricity consumption as well as for the implementation of decision support systems which can lead the decision making process of energy system. Energy consumption time series prediction problems represent a difficult type of predictive modelling problem due to the existence of complex linear and non-linear patterns. This paper presents two approaches for energy consumption forecast: an autoregressive integrated moving average (ARIMA) model and a non-linear autoregressive neural network (NAR) model. The two models are deeply described and finally compared in order to evaluate their performance.