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dc.rights.licenseopenen_US
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorVIOT, Hugo
hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierLa Rochelle Université [ULR]
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorSEMPEY, Alain
IDREF: 123978777
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorMORA, Laurent
IDREF: 077660870
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorBATSALE, Jean-Christophe
hal.structure.identifierInstitut de Mécanique et d'Ingénierie [I2M]
dc.contributor.authorMALVESTIO, Jerome
IDREF: 228223229
dc.date.accessioned2024-09-13T12:43:43Z
dc.date.available2024-09-13T12:43:43Z
dc.date.issued2018-05-05
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/201581
dc.description.abstractEnA simple way to reduce energy consumption is to minimize heating use during unoccupied periods. This implies the possibility of adjusting the room temperature setpoint. However, systems with a large thermal capacity cannot follow sudden setpoint changes because of their thermal inertia. A model predictive con- trol (MPC) allows the harnessing of this inertia in order to reduce heating costs and improve comfort. This advanced control technique is based on disturbances anticipation (occupation, weather conditions) and requires a model of the system which has to be controlled. Therefore, the use of such controller needs a reliable model that describes well the dynamics of the room on upcoming days. This paper presents a method for the selection of the model (type, level of complexity) to be implemented in a MPC controller to anticipate the control of a long time response floor heating system on a real building. The demonstra- tion room and embedded systems serving as experimental support are presented. Short measurement periods are carried out to identify the model parameter values minimizing the gap between model out- put and measurement. Gray-box models based on electrical analogy and state-space representation are proposed. They are constructed from physical knowledge and then identified by choosing the most ap- propriate measurement series. A sensitivity analysis method (Morris) is used to improve the quality of the identified model which satisfies control criteria with two specific validation measurement series. In a complementary paper, the predictive controller integrating the selected model is compared to more conventional management strategies in simulation and on-site with the experimental building.
dc.description.sponsorshipPREdiction et Contrôle Commande Intelligent par la Simulation et l'Optimisation Numérique - ANR-12-VBDU-0006en_US
dc.language.isoENen_US
dc.subject.enLow order model
dc.subject.enBuilding parameter identification
dc.subject.enInstrumentation
dc.subject.enModel predictive control
dc.title.enModel predictive control of a thermally activated building system to improve energy management of an experimental building: Part I—Modeling and measurements
dc.typeArticle de revueen_US
dc.identifier.doi10.1016/j.enbuild.2018.04.055en_US
dc.subject.halSciences de l'ingénieur [physics]/Matériauxen_US
bordeaux.journalEnergy and Buildingsen_US
bordeaux.page94-103en_US
bordeaux.hal.laboratoriesInstitut de Mécanique et d’Ingénierie de Bordeaux (I2M) - UMR 5295en_US
bordeaux.issue172en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionBordeaux INPen_US
bordeaux.institutionCNRSen_US
bordeaux.institutionINRAEen_US
bordeaux.institutionArts et Métiersen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-04697023
hal.version1
hal.date.transferred2024-09-13T12:43:46Z
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exporttrue
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Energy%20and%20Buildings&rft.date=2018-05-05&rft.issue=172&rft.spage=94-103&rft.epage=94-103&rft.au=VIOT,%20Hugo&SEMPEY,%20Alain&MORA,%20Laurent&BATSALE,%20Jean-Christophe&MALVESTIO,%20Jerome&rft.genre=article


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