Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe

A significant part of the housing stock in southern Europe is obsolete and in need of extensive retrofitting to improve its energy performance and thermal comfort. However, before adequate retrofit measures can be proposed for this housing stock, the characterization of current building performance...

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Authors: Escandón Ramírez, Rocío, Ascione, Fabrizio, Bianco, Nicola, Mauro, Gerardo Maria, Suárez, Rafael, Sendra, Juan J.
Format: article
Status:Versión aceptada para publicación
Publication Date:2019
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/150677
Online Access:https://hdl.handle.net/11441/150677
https://doi.org/10.1016/j.applthermaleng.2019.01.013
Access Level:Open access
Keyword:Social housing stock
Thermal comfort
Building performance simulation
Sensitivity analysis
Simulation model calibration
Surrogate models
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spelling Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern EuropeEscandón Ramírez, RocíoAscione, FabrizioBianco, NicolaMauro, Gerardo MariaSuárez, RafaelSendra, Juan J.Social housing stockThermal comfortBuilding performance simulationSensitivity analysisSimulation model calibrationSurrogate modelsA significant part of the housing stock in southern Europe is obsolete and in need of extensive retrofitting to improve its energy performance and thermal comfort. However, before adequate retrofit measures can be proposed for this housing stock, the characterization of current building performance is fundamental. Although the simulation tools frequently used and widely accepted by the scientific community ensure accurate results, these require high computational times. The main aim of this paper is the development of a surrogate model to speed up the thermal comfort prediction for any member of a building category, ensuring high reliability by testing the entire simulation process with real data measured in-situ. To this end, an artificial neural network (ANN) is generated under MATLAB® environment using the data obtained from EnergyPlus simulations for linear-type social housing multi-family buildings in southern Spain, which were constructed in the post-war period. The developed ANN provides a regression coefficient between simulation targets and ANN outputs of 0.96, with a relative error between monitored and simulated data below 9%. A further result is that the building category characterization shows a general lack of suitable indoor thermal comfort conditions, thereby showing the great need for effective retrofit strategies.ElsevierConstrucciones Arquitectónicas ITEP130: Arquitectura, Patrimonio y Sostenibilidad: Acústica, Iluminación, Óptica y EnergíaUniversidad de Sevilla2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/150677https://doi.org/10.1016/j.applthermaleng.2019.01.013reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésApplied Thermal Engineering, 150, 492-505.https://www.sciencedirect.com/science/article/pii/S1359431118360617?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1506772026-06-17T12:51:07Z
dc.title.none.fl_str_mv Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe
title Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe
spellingShingle Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe
Escandón Ramírez, Rocío
Social housing stock
Thermal comfort
Building performance simulation
Sensitivity analysis
Simulation model calibration
Surrogate models
title_short Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe
title_full Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe
title_fullStr Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe
title_full_unstemmed Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe
title_sort Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe
dc.creator.none.fl_str_mv Escandón Ramírez, Rocío
Ascione, Fabrizio
Bianco, Nicola
Mauro, Gerardo Maria
Suárez, Rafael
Sendra, Juan J.
author Escandón Ramírez, Rocío
author_facet Escandón Ramírez, Rocío
Ascione, Fabrizio
Bianco, Nicola
Mauro, Gerardo Maria
Suárez, Rafael
Sendra, Juan J.
author_role author
author2 Ascione, Fabrizio
Bianco, Nicola
Mauro, Gerardo Maria
Suárez, Rafael
Sendra, Juan J.
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Construcciones Arquitectónicas I
TEP130: Arquitectura, Patrimonio y Sostenibilidad: Acústica, Iluminación, Óptica y Energía
Universidad de Sevilla
dc.subject.none.fl_str_mv Social housing stock
Thermal comfort
Building performance simulation
Sensitivity analysis
Simulation model calibration
Surrogate models
topic Social housing stock
Thermal comfort
Building performance simulation
Sensitivity analysis
Simulation model calibration
Surrogate models
description A significant part of the housing stock in southern Europe is obsolete and in need of extensive retrofitting to improve its energy performance and thermal comfort. However, before adequate retrofit measures can be proposed for this housing stock, the characterization of current building performance is fundamental. Although the simulation tools frequently used and widely accepted by the scientific community ensure accurate results, these require high computational times. The main aim of this paper is the development of a surrogate model to speed up the thermal comfort prediction for any member of a building category, ensuring high reliability by testing the entire simulation process with real data measured in-situ. To this end, an artificial neural network (ANN) is generated under MATLAB® environment using the data obtained from EnergyPlus simulations for linear-type social housing multi-family buildings in southern Spain, which were constructed in the post-war period. The developed ANN provides a regression coefficient between simulation targets and ANN outputs of 0.96, with a relative error between monitored and simulated data below 9%. A further result is that the building category characterization shows a general lack of suitable indoor thermal comfort conditions, thereby showing the great need for effective retrofit strategies.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/150677
https://doi.org/10.1016/j.applthermaleng.2019.01.013
url https://hdl.handle.net/11441/150677
https://doi.org/10.1016/j.applthermaleng.2019.01.013
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Applied Thermal Engineering, 150, 492-505.
https://www.sciencedirect.com/science/article/pii/S1359431118360617?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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