Estudo de técnicas de apoio a definições em contratos de energia elétrica

Abstract

The values of the parameters required in a Contract of Electric Power Supply are often estimated with bases on the demand forecasted using the “Naive” method, for nonindustrial consumers. Sometimes, the method suffers some empirical adjustment, which can generate a non-optimal contract for the consumer. Universities (in special the public ones) are examples of these type of consumers since they have considerable physical dimensions. The elaboration of a demand profile for these type of consumers, based on a study of the operation of the facilities, is a complicated task. Because the consumer is part of the Public Service, there is a need for Contracts defined with sufficiently clear criteria, given the pressure of the control bodies, particularly when the responsible and effective use of the public money is considered. Therefore, methods based on the consumer demand forecast in function of consumer’s history, and capable of greater approximation with reality, would be important to obtain contracts with minimized financial values. A method was developed based on data of the registered demands of the Federal University of Juiz de Fora (UFJF), and Temperature and Class Calendar adjuvant data. The method tests the predictions made by linear (Moving Averages, ARIMA and Holt-Winters) and by non-linear methods (Neural Networks). The predictions were compared and the best one was taken to an optimization process using Genetic Algorithms. The optimization revealed optimal data for the contract and its costs. The prediction that showed the best performance was the one obtained using Neural Networks without the adjuvant data. The optimization led to the choice of the “Tarifa Azul”, with possible economic gains for UFJF.