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Analysis of smart thermostat thermal models for residential building

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dc.contributor.authorArias, J.
dc.contributor.authorKhan, A.A.
dc.contributor.authorRodriguez Uría, J.
dc.contributor.authorSama Meige, Miguel Ángel
dc.date.accessioned2024-10-04T06:26:44Z
dc.date.available2024-10-04T06:26:44Z
dc.date.issued2022-06-02
dc.description.abstractThis work studies the thermal behavior of residential buildings by using the data provided by smart thermostats and weather forecast data. We consider an equivalent circuit model depending on four parameters related to the heater power, the solar energy, heat capacity, and the thermal resistance of the building. We employ a random ordinary differential equation to overcome the natural model uncertainty. We develop a differential equation constrained least-squares-based parameter identification approach for uncertainty quantification. We provide results related to the analytic properties of the parameter-to-solution map involving a derivative characterization. Furthermore, using a discretization scheme adapted to the given data, we derive discrete formulas for the deterministic identification model and compute the statistical moments of the random model. Following a machine learning approach, we propose an algorithm that consists of three phases. The first is a training phase where we identify the parameter uncertainties on a training dataset. In the second phase, we establish a normal distribution of the parameters using these uncertainties. In the final phase, we simulate the temperature on a test dataset by solving the random model. We test this algorithm on real data from two residential buildings. The detailed numerical experiments show the feasibility and the efficacy of the developed framework.en
dc.description.versionversión publicada
dc.identifier.citationArias, J., Khan, A. A., Rodriguez-Uría, J., Sama, M. (2022). Analysis of smart thermostat thermal models for residential building. Applied Mathematical Modelling, 110, 241-261. DOI 10.1016/j.apm.2022.05.041.
dc.identifier.doihttps://doi.org/10.1016/j.apm.2022.05.041
dc.identifier.issn1872-8480
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23894
dc.journal.titleApplied Mathematical Modelling
dc.journal.volume110
dc.language.isoen
dc.page.final261
dc.page.initial241
dc.publisherElsevier
dc.relation.centerFacultades y escuelas::Facultad de Ciencias
dc.relation.departmentMatemática Aplicada I
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas
dc.subject.keywordsConstrained quadratic optimizationen
dc.subject.keywordsUncertainty quantificationen
dc.subject.keywordsMachine learningen
dc.subject.keywordsThermal building modelsen
dc.subject.keywordsOrdinary differential equationsen
dc.subject.keywordsParameter estimationen
dc.titleAnalysis of smart thermostat thermal models for residential buildingen
dc.typeartículoes
dc.typejournal articleen
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relation.isAuthorOfPublication8d481509-dd79-41a2-bf87-6e156cab5036
relation.isAuthorOfPublication.latestForDiscovery8d481509-dd79-41a2-bf87-6e156cab5036
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