Detection of heat flux failures in building using a soft computing diagnostic system

Title:

Detection of heat flux failures in building using a soft computing diagnostic system

Creator:

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

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:54878d94-6829-452a-b4b6-e84a6b7b8dfb
uuid:54878d94-6829-452a-b4b6-e84a6b7b8dfb

Subject:

Computational intelligence, soft computing, identification systems, artificial neural networks, non-linear systems, and energetic efficiency

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The detection of insulation failures in buildings could potentially conserve energy supplies and improve future designs. Improvements to thermal insulation in buildings include the development of models to assess fabric gain -heat flux through exterior walls in the building- and heating processes. Thermal insulation standards are now contractual obligations in new buildings, and the energy efficiency of buildings constructed prior to these regulations has yet to be determined. The main assumption is that it will be based on heat flux and conductivity measurement. Diagnostic systems to detect thermal insulation failures should recognize anomalous situations in a building that relate to insulation, heating and ventilation. This highly relevant issue in the construction sector today is approached through a novel intelligent procedure that can be programmed according to local building and heating system regulations and the specific features of a given climate zone. It is based on the following phases. Firstly, the dynamic thermal performance of dif\-ferent variables is specifically modeled. Secondly, an exploratory projection pursuit method called Cooperative Maximum-Likelihood Hebbian Learning extracts the relevant features. Finally, a supervised neural model and identification techniques constitute the model for the diagnosis of thermal insulation failures in building due to the heat flux through exterior walls, using relevant features of the data set. The reliability of the proposed method is validated with real datasets from several Spanish cities in winter time.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:7

Harvested from:

CDK

Metadata only:

false