Short term load forecasting using neural network with rough set

Abstract

Accurate Short term load forecasting (STLF) plays a significant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need, This paper presents an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors in order to develop the accuracy of predictions. Through attribution reduction based on variable precision with rough set, the influence of noise data and weak interdependency data to BP is avoided so the rime taken for training is decreased. Using load time series from a practical system, we tested the accuracy of forecasting in specific days with comparison. © Springer-Verlag Berlin Heidelberg 2006.