Modelling the thermal behaviour of a building facade using deep learning

Abstract

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.

Artificial intelligence for structural glass engineering applications — overview, case studies and future potentials

“…The topic of the building envelope or façade has gained enormous importance in recent years due to the discussion on sustainability and energy saving (Aznar et al 2018), where lately the consideration of the user health, well-being, productivity and interaction with the building/façade was added (Luna-Navarro et al 2020). The building envelope on the one hand side determines design and perception of the building for both users and the public while on the other hand, the building envelope is a significant structural sub-system for occupant comfort and interaction of the user with the environment of the building.…”

Water-Covered Roof Versus Inverted Flat Roof on the Mediterranean Coast: A Comparative Study of Thermal and Energy Behavior

“…From massive and supporting systems, they have evolved towards elements composed of overlapping layers, without any supporting function and with the thermal mass of the enclosures significantly reduced. Consequently, thermal capacity (k), that allows dampening of outside thermal variations in the delimited interior space has also substantially diminished [7]. This reality has not been sufficiently reflected in regulations.…”

The Passivhaus Standard in the Spanish Mediterranean: Evaluation of a House’s Thermal Behaviour of Enclosures and Airtightness

“…In this study, we measured the house's thermal transmittance U value, but we did not analyse the effect of the parameters affecting the dynamic regime of the heat flow through the enclosures, such as thermal admittance, Y, and thermal wave lag [42], df, or the damping factor of the thermal wave, f a . The thermal transmittance U values were obtained according to the method established by the current regulations, the Technical Building Code (CTE) [43].…”

“…It was thus possible to analyse users' feeling of comfort, the 466 temperature gradients in the rooms, and to compare the summer and winter energy demands 467 depending on the characteristics of the enclosures and the Blower Door test. Simulations could also 468 be made by modifying the technical characteristics of the enclosures[62], modifying the air 469 infiltration rate factor n50, and other parameters[42]. As indicated, the interior air setpoint 470 temperatures applied were 21 °C in winter and 24 °C in summer.471 Model of the house in Design Builder.…”