Effect of probabilistic inputs on neural network-based electric load forecasting.

This paper presents a novel method to include the uncertainties or the weather-related input variables in neural network-based electric load forecasting models. The new method consists of traditionally trained neural networks and a set of equations to calculate the mean value and confidence intervals of the forecasted load. This method was tested for daily peak load forecasts for one year by using modified data from a large power system. The tests indicate that in addition to the confidence interval, the new method provides a more accurate mean forecast than a multilayer perceptron networks alone.