Taking into account the uncertainties of prediction in the management of power flows in habitat

This PhD dissertation concerns the power management in buildings. The problem of power management is modeled as a mixed linear program. The work presented in this thesis aims to take into account the uncertainties in the optimization of energy flow in the buildings. The uncertainties are analyzed and two types of uncertainty are identified: parametric uncertainties concerning the vagueness of the parameters (weather forecasts, energy demand forecast ...) and the occurrence uncertainties that are related to uncontrollable actions of the user. A robust optimization approach based on a formulation presented by Bertsimas and Sim for robust linear programming is proposed to take into account the parametric uncertainties. An optimization procedure in two stages, based on stochastic programming, is proposed to answer the occurrence uncertainties. This procedure allows to take into account the energy consumption that is not driven by the management system. The proposed methods have been illustrated on various examples of dwellings. Different energy pricing are addressed.

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Source https://theses.hal.science/tel-00747459
Author Le, Minh Hoang
Maintainer CCSD
Last Updated October 30, 2026, 05:16 (UTC)
Created October 30, 2026, 05:16 (UTC)
Identifier NNT: 2011GRENT067
Language fr
Rights https://about.hal.science/hal-authorisation-v1/
contributor Gestion et Conduite des Systèmes de Production (G-SCOP_GCSP) ; Laboratoire des sciences pour la conception, l'optimisation et la production (G-SCOP) ; Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
creator Le, Minh Hoang
date 2011-10-06T00:00:00
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harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-10-23T00:00:00
relation info:eu-repo/semantics/altIdentifier/doi/10.70675/5844a94ez1e94z45eez9c27zf4b6bf3edce7
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