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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorKar, Koushik
dc.contributorMishra, Sandipan
dc.contributorWen, John T.
dc.contributor.authorTariq, Zaid Bin
dc.date.accessioned2021-11-03T09:09:00Z
dc.date.available2021-11-03T09:09:00Z
dc.date.created2019-07-12T15:35:30Z
dc.date.issued2019-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2376
dc.descriptionMay 2019
dc.descriptionSchool of Engineering
dc.description.abstractIn multi-zone indoor spaces with thermal dependencies between zones and limited heating/cooling sources, choosing optimal zonal thermostat or heat input settings is often not enough to attain desired zonal temperatures. In the first part of the thesis, the problem of determining how the thermal flow in-between the different zones of the indoor space, or between the zones and the ambient, should be adapted towards improving the overall thermal environment in the multi-zone space. Despite the complex structure of the problem, this thesis shows how the optimal zonal heat transfer adaptation problem -- which involves determining the optimal zonal heat input settings as well -- can be formulated and solved efficiently. The work also determines conditions under which the problem has a convex structure. Simulations on a 6-zone indoor space model show that zonal heat transfer adaptation can result in significant improvement of zonal temperatures and/or energy costs, particularly when not all zones are equipped with heat sources. The second part of the thesis provides experimental evaluation of a learning-based data prediction model for predictable thermal control of a shared indoor space. Data from a smart conference room experimental test bed is used for learning the black-box model which is then incorporated into finite horizon control problem for controlling heating and cooling inputs of the indoor space. The results show that the data-driven predictive controller can maintain temperatures closer to that desired by occupants than thermostat based non-predictive control implemented by the building thermal management systems.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectComputer Systems engineering
dc.titleEnabling better thermal management in multi-zone indoor spaces
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid179594
dc.digitool.pid179595
dc.digitool.pid179596
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreeMS
dc.relation.departmentDept. of Electrical, Computer, and Systems Engineering


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