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

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外文摘要：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.

外文关键词：

clothing insulation simulationadaptive thermal comfortbehavioural adaptive actionsmachine learning

作者：

Pablo Aparicio-Ruiz、Elena Barbadilla-Martín、José Guadix、Jesús Mu?uzuri

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作者单位：

Grupo de Ingeniería de Organización,Escuela Técnica Superior de Ingeniería,Universidad de Sevilla,Camino de los Descubrimientos S/N,41092,Seville,Spain

基金：

SICODE project funded by the I+D+i FEDER project in Andalusia 2014-2020CONFORES project funded by Proyectos de Transición Ecológica y Transición DigitalFunding for open access publishing:Univer sidad de Sevilla/CBUA

项目编号：

Ref.US-1380581Ref.TED2021-130659B-I00

出版年：

2024

DOI：

10.1007/s12273-024-1114-9

建筑模拟(英文版)

ISSN：1996-3599

年,卷(期)：2024.17(5)