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Méta-optimisation pour la calibration automatique de modèles énergétiques bâtiment pour le pilotage anticipatif

Abstract : In order to tackle the actual climate issues, the building field is encouraged to reduce his energetic consumption without changing the occupant’s comfort. In this context, the aim of the ANR PRECCISION project is to develop tools and methods for energetic management of the buildings which needs the use of dynamical thermal models. The PHD works, realise between the G2Elab and the G-SCOP, was focused on models parametric estimation issues. Indeed, uncertainties due to unknown phenomena and the nature of models lead to difficulties for the calibration of the models. Nowadays, this complex procedure is still not automatable: auto-regressive models have a low capacity to extrapolate because of their inadequate structure, whereas the physical models are non-linear regarding many parameters: estimations lead towards local optimums which highly depend on the initial point. In order to eliminate these constraints, several approaches have been explored with physical models adapted for which identifiability studies have been reached on an experimental platform: PREDIS MHI. Different optimisation strategies will be proposed in order to determine the parameters which can be estimated. The first approach uses an analyse a priori of the parametric dispersion, the second one use a meta optimisation which dynamicaly determined as the optimisation sequence, the parameters which can be readjusted. The results are analysed and compared to several approaches (universal models, “simple” identification of all the parameters of a physical model, genetic algorithm …) in different application cases.
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Submitted on : Friday, August 26, 2016 - 11:59:31 AM
Last modification on : Tuesday, October 6, 2020 - 4:30:21 PM
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  • HAL Id : tel-01356728, version 1



Audrey Le Mounier. Méta-optimisation pour la calibration automatique de modèles énergétiques bâtiment pour le pilotage anticipatif. Energie électrique. Université Grenoble Alpes, 2016. Français. ⟨NNT : 2016GREAT038⟩. ⟨tel-01356728⟩



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