Building a 'quasi optimal' neural network to solve the short-term load forecasting problem

Abstract

The ability to solve the short-term load forecasting (STLF) problem with artificial neural networks (ANNs) is investigated by conducting a fractional factorial experiment. The results of the experiment are analyzed, and the factors and factor interactions that affect forecast errors are identified and quantified. From the analysis, we derive rules for building a 'quasi optimal' neural network to solve the STLF problem. A comparison study demonstrates the superior performance of the 'quasi optimal' neural network over an automated Box-Jenkins seasonal ARIMA model in solving the STLF problem.