Short term load forecasting using a Bayesian combination method

Abstract

This paper presents the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for Short Term Load Forecasting (STLF) based on the combination of an artificial neural network (ANN) predictor and two linear regression (LR) predictors. The method is applied to STLF for the Greek Public Power Corporation dispatching center of the island of Crete, using 1994 data, and daily load profiles are obtained. Statistical analysis of prediction errors reveals that during given time periods the ANN predictor consistently produces better forecasts for certain hours of the day, while the LR predictor produces better forecasts for the rest. This relative prediction advantage may change over different time intervals. The combined prediction is a weighted sum of the ANN and LR predictions, where the weights are computed using an adaptive update of the Bayesian posterior probability of each predictor, based on their past predictive performance. The proposed method outperforms both ANN and LR predictions.