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- W57396989 abstract "Through 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 follow a normal probability distribution. In this paper, the 1-24 steps ahead load forecasts are obtained through MultiLayer Perceptrons trained by the back-propagation 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." @default.
- W57396989 created "2016-06-24" @default.
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- W57396989 date "2016-03-17" @default.
- W57396989 modified "2023-09-25" @default.
- W57396989 title "Neural Network Based Short-Term Electric Load Forecasting with Confidence Intervals" @default.
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- W57396989 doi "https://doi.org/10.21528/cbrn1999-002" @default.
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