Bayesian neural network approach to short time load forecasting

Abstract

Short term load forecasting (STLF) is an essential tool for efficient power system planning and operation. We propose in this paper the use of Bayesian techniques in order to design an optimal neural network based model for electric load forecasting. The Bayesian approach to modelling offers significant advantages over classical neural network (NN) learning methods. Among others, one can cite the automatic tuning of regularization coefficients, the selection of the most important input variables, the derivation of an uncertainty interval on the model output and the possibility to perform a comparison of different models and, therefore, select the optimal model. The proposed approach is applied to real load data.