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Improving Energy Efficiency in Buildings Using Machine Intelligence

  • Javier Sedano
  • José Ramón Villar
  • Leticia Curiel
  • Enrique de la Cal
  • Emilio Corchado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

Improving the detection of thermal insulation in buildings –which includes the development of models for heating and ventilation processes and fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon footprints of domestic heating systems. Thermal insulation standards are now contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those standards. Lighting, occupancy, set point temperature profiles, air conditioning and ventilation services all increase the complexity of measuring insulation efficiency. The identification of thermal insulation failure can help to reduce energy consumption in heating systems. Conventional methods can be greatly improved through the application of hybridized machine learning techniques to detect thermal insulation failures when a building is in operation. A three-step procedure is proposed in this paper 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 modelled, for each building type and climate zone. Secondly, Cooperative Maximum-Likelihood Hebbian Learning is used to extract the relevant features. Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter zone C cities in Spain. Although a great deal of further research remains to be done in this field, the proposed system is expected to outperform conventional methods described in Spanish building codes that are used to calculate energetic profiles in domestic and residential buildings.

Keywords

Feature Selection Heating System Machine Intelligence Improve Energy Efficiency Indoor Temperature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Javier Sedano
    • 1
  • José Ramón Villar
    • 2
  • Leticia Curiel
    • 3
  • Enrique de la Cal
    • 2
  • Emilio Corchado
    • 3
  1. 1.Department of Electromechanical EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of Computer ScienceUniversity of OviedoSpain
  3. 3.Department of Civil EngineeringUniversity of BurgosBurgosSpain

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