Very Short-Term Load Forecasting Based on ARIMA Model and Intelligent Systems

Abstract

The main purpose of this paper is to achieve a comparative analysis among autoregressive integrated moving average model, artificial neural networks and adaptive neurofuzzy system techniques for load demand forecasting in distribution substations. The system inputs are three load demand time series, which are composed by data measured at intervals of five minutes each, during seven days, from substations located at Andradina, Ubatuba and Votuporanga. Autoregressive integrated moving average models with suitable results have been analyzed, whereas several input configurations and different architectures have been investigated for artificial neural networks and adaptive neuro-fuzzy system techniques aiming the forecasting of twelve further steps. The results showed the artificial neural network based technique superiority for such forecasting, followed by autoregressive integrated moving average model and adaptive neuro-fuzzy approach. The load demand forecasting can minimize costs of energy generation as well as improve the electric power system safety.