Research on thermal comfort has revealed various adaptive behaviours in a hostel room,such as changing cloth-ing,use of windows,doors,and ceiling fans.Hostel rooms are used for various activities and are typically fur...Research on thermal comfort has revealed various adaptive behaviours in a hostel room,such as changing cloth-ing,use of windows,doors,and ceiling fans.Hostel rooms are used for various activities and are typically fur-nished with a wardrobe,bed,study table,and chair.Recent studies indicate that ceiling fan fixed at the centre of the room may not provide adequate air velocity for different activities occurring in different parts of a room.Although students generally arrange furniture based on their preferences and room geometry,the influence of fan-induced air on furniture layout to improve thermal comfort is yet to be established.In this context,this study investigates spatial adaptation and identifies the factors affecting furniture layout preferences in hostel rooms.In a yearlong study,patterns of furniture layout were observed in twenty-one naturally ventilated hostel buildings to find their relationship with environmental and non-environmental factors.A total of 1665 observation data was collected from single,double,and triple occupancy rooms.Influence of various factors on arranging the furniture was identified through a questionnaire survey.Throughout the survey,outdoor temperature varied between 23 and 41°C and outdoor relative humidity varied between 32.3%and 97.5%.The spatial arrangement of furni-ture was evaluated against fan location.Results indicate that fan location and indoor temperature significantly influence the furniture arrangement.A logistic regression equation was developed to evaluate the trigger temper-ature when students began moving furniture towards ceiling fan.In a single occupancy room,above 34.2°C,the probability of moving the bed towards the fan was found to be maximum.In single and double occupancy rooms,students move the bed near the ceiling fan predominantly during night-time to get sufficient air movement.A cautious design of furniture layout and adding a personalised fan for various activities may improve the thermal comfort in hostel rooms.展开更多
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 recommenda...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.展开更多
文摘Research on thermal comfort has revealed various adaptive behaviours in a hostel room,such as changing cloth-ing,use of windows,doors,and ceiling fans.Hostel rooms are used for various activities and are typically fur-nished with a wardrobe,bed,study table,and chair.Recent studies indicate that ceiling fan fixed at the centre of the room may not provide adequate air velocity for different activities occurring in different parts of a room.Although students generally arrange furniture based on their preferences and room geometry,the influence of fan-induced air on furniture layout to improve thermal comfort is yet to be established.In this context,this study investigates spatial adaptation and identifies the factors affecting furniture layout preferences in hostel rooms.In a yearlong study,patterns of furniture layout were observed in twenty-one naturally ventilated hostel buildings to find their relationship with environmental and non-environmental factors.A total of 1665 observation data was collected from single,double,and triple occupancy rooms.Influence of various factors on arranging the furniture was identified through a questionnaire survey.Throughout the survey,outdoor temperature varied between 23 and 41°C and outdoor relative humidity varied between 32.3%and 97.5%.The spatial arrangement of furni-ture was evaluated against fan location.Results indicate that fan location and indoor temperature significantly influence the furniture arrangement.A logistic regression equation was developed to evaluate the trigger temper-ature when students began moving furniture towards ceiling fan.In a single occupancy room,above 34.2°C,the probability of moving the bed towards the fan was found to be maximum.In single and double occupancy rooms,students move the bed near the ceiling fan predominantly during night-time to get sufficient air movement.A cautious design of furniture layout and adding a personalised fan for various activities may improve the thermal comfort in hostel rooms.
基金the financial support of the SICODE project(Ref.US-1380581)funded by the I+D+i FEDER project in Andalusia 2014-2020the CONFORES project(Ref.TED2021-130659B-I00)funded by Proyectos de Transición Ecológica y Transicion Digital.
文摘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.
