Implementing a weighted least squares procedure in training a neural network to solve the short-term load forecasting problem

Abstract

The use of a weighted least squares procedure when training a neural network to solve the short-term load forecasting (STLF) problem is investigated. Our results indicate that a neural network that implements the weighted least squares procedure outperforms a neural network that implements the least squares procedure during the on-peak period for the two performance criteria specified; MAE% and COST, during the entire period for the COST criterion. It is therefore, recommended that the weighted least squares procedure be further studied by electric utilities which use neural networks to forecast their short-term load, and experience large variabilities in their hourly marginal energy costs during a 24-hour period.