People's clothing behaviour according to external weather and indoor environment

Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures

“…It is not important which one is kept. We arbitrarily preserved the minimum air temperature (6:00 o'clock) to maintain consistency with previous work [7,12] where it was hypothesized that morning temperatures are the most important because in the morning people usually select their clothing. The air velocities are strongly correlated, too (Spearman's rank between 0.46 and 0.67).…”

“…A model that is able to predict how building occupants change their clothing would greatly improve HVAC system operation. Previous attempts to develop a dynamic clothing model demonstrated that the ability to more accurately predict variations in clothing leads to improved thermal comfort [2], smaller HVAC size and lower energy consumption [7].…”

“…As explained later in the paper, it is possible to take into account the variance caused by the building and use each occupant as the statistical unit by applying regression analysis based on mixed models (fixed plus random effects) instead of linear model (only a fixed effect) [10]. De Carli et al [7] developed single variable linear regression models to predict the clothing insulation as a function of the outdoor air temperature measured at 6 o'clock in the morning. Independent models were developed for naturally and mechanically air conditioned buildings and for three latitudes ranges.…”

“…The models were based on the database developed within ASHRAE research project RP-884 [9] and on field measurements performed by Feriadi et al [11]. Based on energy simulation, De Carli et al [7] concluded that in mechanically conditioned buildings a variation of 0.1 clo is sufficient to significantly affect the comfort evaluation based on the PMV-PPD model. The developed models have the following limitations: a) the homoscedasticity hypothesis of the developed linear models has not been reported, therefore it is possible that the regression coefficients are not correct [10]; b) the variance introduced by the building was not included in the models; c) all the data from ASHRAE RP-884 was used regardless of the quality of the measurements of the single projects included in the final database and the fact that different standards have been used to quantify the clothing insulation [9]; d) single variable regression models were used, losing the opportunity to check for interaction effect and the combination of several variables at the same time; and e) it is not clear if other relevant variables, such as air velocity and relative humidity were considered.…”

Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control

“…A comfortable indoor environment for all the occupants in a building is difficult to reach because of individual differences between those occupants. Based on the literature, it is concluded that individual differences, including: age (van Oeffelen, 2007), gender (Choi, Aziz, & Loftness, 2010;Karjalainen, 2007), percentage body fat (Zhang, Huizenga, Arens, & Yu, 2001), metabolism (Havenith, Holmér, & Parson, 2002) and clothing resistance (De Carli, Olesen, Zarrella, & Zecchin, 2007), are of importance for the individual experienced thermal comfort. However, nowadays the Fanger comfort model (Fanger, 1970) is still mainly used to determine the (thermal) comfort inside office buildings.…”