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

作　　者：Pablo Aparicio-Ruiz Elena Barbadilla-Martin Jose Guadix Jesus Munuzuri 

机构地区：[1]Grupo de Ingenieria de Organizacion,Escuela Tecnica Superior de Ingenieria,Universidad de Sevilla,Camino de los Descubrimientos S/N,41092,Seville,Spain

出　　处：《Building Simulation》2024年第5期839-855,共17页建筑模拟（英文）

基　　金：the financial support of the SICODE project(Ref.US-1380581)funded by the I+D+i FEDER project in Andalusia 2014-2020;the CONFORES project(Ref.TED2021-130659B-I00)funded by Proyectos de Transición Ecológica y Transicion Digital.

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.

关 键 词：clothing insulation simulation adaptive thermal comfort behavioural adaptive actions machine learning 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]