Load forecasting dealing with medium voltage network reconfiguration

Planing the operation in modern power systems requires suitable anticipation of load evolution at different levels of distribution network. Under this perspective, load forecasting performs an important task, allowing the optimization of investments and the adequate exploitation of existing distribution networks. This paper describes the models developed for current intensity forecasting at primary substation feeders. The main goal consists on defining a regression process characterized by good quality estimates of those future intensity values, based on historical database. Anticipation interval shall include from the next hour to one week in advance. The forecasting method shall also be adaptable to power network reconfiguration, whenever planned or not. In this work, artificial neural networks (ANN) were used as the basic regression tool. This paper describes used ANN as well as the premises that led to the implementation of selected forecasting models. At last, some illustrative results attained so far are presented, supporting the adequacy of adopted approach.