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Confidence intervals for neural network based short-term load forecasting

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2 Author(s)
da Silva, A.P.A. ; Fed. Eng. Sch. at Itajuba, Brazil ; Moulin, L.S.

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: (1) error output; (2) resampling; and (3) multilinear regression adapted to neural networks. A comparison of the three techniques is performed through simulations of online forecasting

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Power Systems, IEEE Transactions on  (Volume:15 ,  Issue: 4 )