Short Term Load Forecasting Using Predictive Modular Neural Networks

1 Abstract In this paper we present an application of predictive modular neural networks (PREMONN) to short term load forecasting. PREMONNs are a family of probabilistically motivated algorithms which can be used for time series prediction, classification and identification. PREMONNs utilize local predictors of several types (e.g. linear predictors or artificial neural networks) and produce a final prediction which is a weighted combination of the local predictions; the weights can be interpreted as Bayesian posterior probabilities and are computed online. The method is applied to short term load forecasting for the Greek Public Power Corporation dispatching center of Crete, where PREMONN outperforms conventional prediction techniques.