Nonlinear Models for Short-time Load Forecasting - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Energy Procedia Année : 2012

Nonlinear Models for Short-time Load Forecasting

Résumé

Short Term Load Forecasting (STLF) is essential for planning the day-to-day operation of an electric power system. As this forecasting leads to increased security operation's conditions and economic cost savings, numerous techniques have been used to improve the STLF. We propose in this paper the comparison of two nonlinear regression techniques namely Gaussian Process (GP) regression models and Neural Network (NN) models. While the Bayesian approach to NN modelling offers significant advantages over the classical NN learning methods, it will be shown that the use of GP regression models will improve the performances of the forecasting. The proposed techniques are applied to real load data.
Fichier principal
Vignette du fichier
Nonlinear_models_load_forecasting.pdf (368.47 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-01090088 , version 1 (15-06-2018)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Philippe Lauret, Mathieu David, Didier Calogine. Nonlinear Models for Short-time Load Forecasting. Energy Procedia, 2012, 14, pp.1404-1409. ⟨10.1016/j.egypro.2011.12.1109⟩. ⟨hal-01090088⟩
130 Consultations
107 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More