Two-stage neural network approach to precise 24-hour load pattern prediction

Abstract

The paper presents the neural network approach to the precise 24-hour load pattern prediction for the next day in the power system. In this approach we use the ensemble of few neural network predictors working in parallel. The predicted series containing 24 values of the load pattern generated by the neural predictors are combined together using principal component analysis. Few principal components form the input vector for the final stage predictor composed of another neural network. The developed system of prediction was tested on the real data of the Polish Power System. The results have been compared to the appropriate values generated by other methods. © 2009 Springer Berlin Heidelberg.