Confidence intervals for neural network based short-term load forecasting

Using traditional statistical models, like ARMA and Multilinear Regression, confidence intervals can be computed for the short-term electric load forecasting, assuming that the forecast errors are independent and Gaussian distributed. In this paper, the 1 to 24 steps ahead load forecasts are obtained through multilayer perceptrons trained by the backpropagation algorithm, Three techniques for the computation of confidence intervals for this neural network based short-term load forecasting are presented: i) Error Output, ii) Resampling and iii) Multilinear Regression adapted to neural networks. A comparison of the three techniques is performed through simulations of on-line forecasting.