Open AccessModelling the thermal behaviour of a building facade using deep learning
Author(s) -
Fidel Aznar Gregori,
V. Echarri,
Carlos Rizo,
Ramón Rizo Aldeguer
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0207616
Subject(s) - enclosure , facade , computer science , thermal , work (physics) , fourier transform , energy consumption , energy (signal processing) , simulation , artificial intelligence , mechanical engineering , mathematics , engineering , meteorology , statistics , physics , structural engineering , mathematical analysis , telecommunications , electrical engineering
This article aims to model the thermal behaviour of a wall using deep learning techniques. The Fourier theoretical model which is traditionally used to model such enclosures is not capable of considering several factors that affect a prediction that is often incorrect. These results motivate us to try to obtain a better thermal model of the enclosure. For this reason, a connexionist model is provided capable of modelling the behaviour of the enclosure from actual observed temperature data. For the training of this model, several measurements have been obtained over the course of more than one year in a specific enclosure, distributing the readings among the different layers of it. In this work, the predictions of both the theoretical model and the connexionist model have been tested, contrasting them with the measurements obtained previously. It has been observed that the connexionist model substantially improves the theoretical predictions of the Fourier method, thus allowing better approximations to be made regarding the real energy consumption of the building and, in general, the prediction of the energy behaviour of the enclosure.