Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach

This article is licensed under a Creative Commons Attribution 4.0 International License.

Bibliographic Details
Authors: Aparicio Ruiz, Pablo, Barbadilla Martín, Elena, Guadix Martín, José, Muñuzuri, Jesús
Format: article
Status:Published version
Publication Date:2024
Country:España
Institution:Universidad de Sevilla (US)
Repository:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/157463
Online Access:https://hdl.handle.net/11441/157463
https://doi.org/10.1007/s12273-024-1114-9
Access Level:Open access
Keyword:Clothing insulation simulation
Adaptive thermal comfort
Behavioural adaptive actions
Machine learning
id ES_b050eb656edd94e269ab9ae3cb0fe7df
oai_identifier_str oai:idus.us.es:11441/157463
network_acronym_str ES
network_name_str España
repository_id_str
spelling Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approachAparicio Ruiz, PabloBarbadilla Martín, ElenaGuadix Martín, JoséMuñuzuri, JesúsClothing insulation simulationAdaptive thermal comfortBehavioural adaptive actionsMachine learningThis article is licensed under a Creative Commons Attribution 4.0 International License.Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.Springer LinkOrganización Industrial y Gestión de Empresas IITEP127: Ingeniería de la OrganizaciónEuropean Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)Universidad de Sevilla2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/157463https://doi.org/10.1007/s12273-024-1114-9reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésBuilding Simulation, 17, 839-855.US-1380581TED2021-130659B-I00https://link.springer.com/article/10.1007/s12273-024-1114-9info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1574632026-06-17T12:51:07Z
dc.title.none.fl_str_mv Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
title Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
spellingShingle Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
Aparicio Ruiz, Pablo
Clothing insulation simulation
Adaptive thermal comfort
Behavioural adaptive actions
Machine learning
title_short Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
title_full Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
title_fullStr Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
title_full_unstemmed Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
title_sort Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach
dc.creator.none.fl_str_mv Aparicio Ruiz, Pablo
Barbadilla Martín, Elena
Guadix Martín, José
Muñuzuri, Jesús
author Aparicio Ruiz, Pablo
author_facet Aparicio Ruiz, Pablo
Barbadilla Martín, Elena
Guadix Martín, José
Muñuzuri, Jesús
author_role author
author2 Barbadilla Martín, Elena
Guadix Martín, José
Muñuzuri, Jesús
author2_role author
author
author
dc.contributor.none.fl_str_mv Organización Industrial y Gestión de Empresas II
TEP127: Ingeniería de la Organización
European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER)
Universidad de Sevilla
dc.subject.none.fl_str_mv Clothing insulation simulation
Adaptive thermal comfort
Behavioural adaptive actions
Machine learning
topic Clothing insulation simulation
Adaptive thermal comfort
Behavioural adaptive actions
Machine learning
description This article is licensed under a Creative Commons Attribution 4.0 International License.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/157463
https://doi.org/10.1007/s12273-024-1114-9
url https://hdl.handle.net/11441/157463
https://doi.org/10.1007/s12273-024-1114-9
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Building Simulation, 17, 839-855.
US-1380581
TED2021-130659B-I00
https://link.springer.com/article/10.1007/s12273-024-1114-9
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 Springer Link
publisher.none.fl_str_mv Springer Link
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
_version_ 1869416797686464512
score 15,300719

Similar Items