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The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings


Reference:

Ramallo-González, A. P., Brown, M., Gabe-Thomas, E., Lovett, T. and Coley, D. A., 2017. The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings. Journal of Building Performance Simulation

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    Official URL:

    http://dx.doi.org/10.1080/19401493.2016.1273390

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    Abstract

    The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO2 concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.

    Details

    Item Type Articles
    CreatorsRamallo-González, A. P., Brown, M., Gabe-Thomas, E., Lovett, T. and Coley, D. A.
    DOI10.1080/19401493.2016.1273390
    Related URLs
    URLURL Type
    http://www.scopus.com/inward/record.url?scp=85009826797&partnerID=8YFLogxKUNSPECIFIED
    Uncontrolled Keywordsenergy efficiency,inverse modelling,lumped parameter model,smart metre
    DepartmentsFaculty of Science > Computer Science
    Faculty of Humanities & Social Sciences > Psychology
    Faculty of Engineering & Design > Architecture & Civil Engineering
    Research CentresCentre for Doctoral Training in Decarbonisation of the Built Envinronment (dCarb)
    EPSRC Centre for Doctoral Training in Statistical Mathematics (SAMBa)
    Publisher Statementpreprint_final_all_2.pdf: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Building Performance Simulation on 17 Jan 2017, available online: http://www.tandfonline.com/10.1080/19401493.2016.1273390.
    RefereedYes
    StatusPublished
    ID Code54460

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