Statistical models describing the energy signature of buildings

Peder Bacher, Henrik Madsen, Anders Thavlov

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearch

    Abstract

    Approximately one third of the primary energy production in Denmark is used for heating in buildings. Therefore efforts to accurately describe and improve energy performance of the building mass are very important. For this purpose statistical models describing the energy signature of a building, i.e. the heat dynamics of the building, have been developed. The models can be used to obtain rather detailed knowledge of the energy performance of the building and to optimize the control of the energy consumption for heating, which will be vital in conditions with increasing fluctuation of the energy supply or varying energy prices. The paper will give an overview of statistical methods and applied models based on experiments carried out in FlexHouse, which is an experimental building in SYSLAB, Risø DTU. The models are of different complexity and can provide estimates of physical quantities such as UA-values, time constants of the building, and other parameters related to the heat dynamics. A method for selecting the most appropriate model for a given building is outlined and finally a perspective of the applications is given. Aknowledgements to the Danish Energy Saving Trust and the Interreg IV ``Vind i Øresund'' project.
    Original languageEnglish
    Title of host publicationDynamic Methods for Building Energy Assessment
    Publication date2010
    Publication statusPublished - 2010
    EventDYNASTEE International Workshop on Dynamic Methods for Building Energy Assessment - Brussels, Belgium
    Duration: 11 Oct 201012 Oct 2010

    Workshop

    WorkshopDYNASTEE International Workshop on Dynamic Methods for Building Energy Assessment
    CountryBelgium
    CityBrussels
    Period11/10/201012/10/2010

    Keywords

    • Intelligent energy systems
    • Continuous time modelling, heat consumption, smart meters, Grey-box models, heat dynamics, thermal dynamics, buildings, model selection

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