Artificial neural network-based short-term demand forecaster.

Abstract

The importance of Short-Term Load Forecasting (STLF) has been increasing lately. With deregulation and competition, energy price forecasting has become a big business. Bus load forecasting is essential to feed analytical methods utilized for determining energy prices. The variability and non-stationarity of loads are becoming worse due to the dynamics of energy tariffs. Besides, the number of nodal loads to be predicted does not allow frequent interventions from load forecasting experts. More autonomous load predictors are needed in the new competitive scenario. The application of neural network-based STLF has developed sophisticated practical systems over the years. However, the question of how to maximize the generalization ability of such machines, together with the choice of architecture, activation functions, training set data and size, etc. makes up a huge number of possible combinations for the final Neural Network (NN) design, whose optimal solution has not been figured yet. This paper describes a STLF system which uses a non-parametric model based on a linear model coupled with a polynomial network, identified by pruning/growing mechanisms. The load forecaster has special features of data preprocessing and confidence intervals calculations, which are also described. Results of load forecasts are presented for one year with forecasting horizons from 15 min. to 168 hours ahead