Modelagem Fuzzy para previsão de uma série temporal de energia elétrica.

Abstract

This dissertation tests and compares two types of predicting models to the same time series. A time series of electricity distribution was observed and, as a case study, were opted for the metropolitan region of Bahia State. Three exogenous variables were tested in each model: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. For better performance, in each model was tested all the exogenous variables to fit the influence in the energy distributed. According to the evaluation of the tests, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the tests also indicated different exogenous variables for each forecast model.