Modelling of Heat Flux in Building Using Soft-Computing Techniques View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Javier Sedano , José Ramón Villar , Leticia Curiel , Enrique de la Cal , Emilio Corchado

ABSTRACT

Improving the detection of thermal insulation failures in buildings includes the development of models for heating process and fabric gain -heat flux through exterior walls in the building-. Thermal insulation standards are now contractual obligations in new buildings, the energy efficiency in the case of buildings constructed before the regulations adopted is still an open issue, and the assumption is that it will be based on heat flux and conductivity measurement. A three-step procedure is proposed in this study that begins by considering the local building and heating system regulations as well as the specific features of the climate zone. Firstly, the dynamic thermal performance of different variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, a supervised neural model and identification techniques are applied, in order to detect the heat flux through exterior walls in the building. The reliability of the proposed method is validated for a winter zone, associated to several cities in Spain. More... »

PAGES

636-645

References to SciGraph publications

  • 2004-05. Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2008. Minimizing Energy Consumption in Heating Systems under Uncertainty in HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS
  • 2009. A Thermodynamical Model Study for an Energy Saving Algorithm in HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS
  • 2005. Artificial Neural Networks in DO SMART ADAPTIVE SYSTEMS EXIST?
  • 1968-12. On the use of a linear model for the identification of feedback systems in ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
  • Book

    TITLE

    Trends in Applied Intelligent Systems

    ISBN

    978-3-642-13032-8
    978-3-642-13033-5

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-13033-5_65

    DOI

    http://dx.doi.org/10.1007/978-3-642-13033-5_65

    DIMENSIONS

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