Electricity spot price forecasting in Brazil using a hybrid neuro-fuzzy system and neural network approach

Abstract

Modeling and forecasting highly volatile time series is a complex task not suited for linear models. An example of such series is the electricity spot prices in Brazil. In this article we apply a hybrid neuro-fuzzy/neural network system to forecast the weekly spot in the Southeast region of Brazil up to six weeks in advance. The input variables used are lagged values of the Natural Inflow Energy (for the entire region and its two main basins) and of the spot prices themselves. The hybrid system starts by fitting a neural network to data; the result of this network is then saved and used as an additional input at an ANFIS type neuro-fuzzy structure. The forecasting performance of the proposed hybrid model is presented for different periods and the results discussed.