The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global ...Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS – observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15–25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.
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•Assessed PMV-PPD accuracy using the ASHRAE Global Thermal Comfort Database II.•PMV predicted thermal sensation correctly only one out of three times.•PMV had a mean absolute error of one unit on the thermal sensation scale.•PPD was not able to predict the dissatisfaction rate.•PMV-PPD accuracy varied strongly between ventilation, building types and climate.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•1040 valid measurement data and subjective questionnaires were obtained.•Acceptable temperature range of older people in all seasons was assessed.•Existing adaptive thermal comfort models were not ...suitable for older people.•Adaptive thermal comfort models of older people in different seasons were established.•The prediction of winter and summer model were both reliable.
This research focused on adaptive thermal comfort in homes for older people in the hot summer and cold winter climate zone of China. A field study was conducted throughout the four seasons beginning in January 2014 and ending in April 2017 in Shanghai, China. The survey included simultaneous measurements of outdoor and indoor environmental parameters and an assessment of the participants’ sensations using questionnaires. A total of 19 homes for older people and 64 buildings were surveyed; 1040 measured data points and subjective questionnaires were obtained. The sensation ratings were analyzed, and thermal comfort temperature was calculated using regression methods. Results showed that thermal comfort assessments of older people in Shanghai should use –0.2 < TSV < +0.2 as the standard, yielding acceptable temperature ranges for older people of 14.1–19.4 ℃ in winter, 23.8–27.0 ℃ in summer, and 20.6–31.7 ℃ in mid-season. Adaptive thermal comfort models in different seasons showed that, first, the predictions of the winter and summer models were both reliable. Second, the thermal adaptive models of the mid-season intersected with the ASHRAE 55 model at 20.5 ℃ and was within the applicable range of the mid-season model (10–20.8 ℃); under the same outdoor temperature conditions, the neutral temperatures predicted by the EN15251 model and the ASHRAE 55 model were higher than the predicted values of the mid-season thermal adaptive model. Third, the slopes of both the winter and summer thermal adaptive models were slightly lower than those of the GB/T50785-2012 model. These results can be used to assess indoor thermal comfort in homes for older people and help create age-friendly building environments in China.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The understanding of human outdoor thermal comfort demand and thermal adaptation contributes to sustainable urban design as well as city resilience in the context of human health and wellbeing. ...Humans' past thermal experience influence their outdoor thermal comfort. However, the quantitative relationship between the past thermal experience and outdoor thermal comfort is still not clear. This study aims to reveal quantitative relations of the impact of people's past thermal experience on adaptive thermal comfort and to develop a new outdoor adaptive thermal comfort model. A year-long longitudinal questionnaire survey along with a combination of outdoor thermal environment campaigns was carried out in Chongqing, China. It began on August 15, 2020, and finished on August 19, 2021. Through the analysis of 2240 valid responses to the questionnaire survey, the outdoor thermal adaptation characteristic and dynamic thermal comfort evaluation of the respondents were revealed. The results show that the quantified temperature of past outdoor thermal experience is the quadratic correlation with the thermal sensitivity coefficient and deviation constant, and the linear correlated with outdoor thermal demands. Based on the quantitative analysis, a new outdoor adaptive thermal comfort model has been developed as a function of the exponentially weighted sum of historical mean air temperature series (MeanTrm). The outdoor adaptive thermal comfort zones by 80% and 90% satisfactions thereby have been first drawn based on the Universal Thermal Climate Index (UTCI). The study developed a methodology for the evaluation of dynamic outdoor thermal comfort which can be used for different climate regions.
•A year-long longitudinal questionnaire survey with outdoor thermal environment campaigns.•Quantitative evidence of past thermal experiences on outdoor thermal sensation.•Developed a new outdoor adaptive thermal comfort model.•Determined the outdoor adaptive thermal comfort zones.
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The growth of cities intensifies the urban heat island effect by obstructing and weakening the incoming wind and thus deteriorates thermal comfort in the pedestrian level. The elevated building ...design is believed to be able to create some localized comfort spots at precinct scale, but no researches on pedestrians' thermal perceptions in the area underneath an elevated building (UEB) have been reported. In this study, simultaneous on-site meteorological measurements and questionnaire surveys of 1107 human subjects were conducted in a university campus in Hong Kong. Three outdoor thermal comfort models, PET, UTCI and UC-Berkeley model, were compared. The survey results indicate that the UEB area is significantly (α = 0.05) more comfortable in hot weather without extra discomfort in cold weather. All three models outputs correlate well with the subjects' mean thermal sensation votes in linear regression (R2 ≈ 0.9). Yet, shifts in neutral indices (6.2 K, 5.8 K and 1.1 respectively for PET, UCTI and UC-Berkeley model) appeared when comparing the correlation results separately for the UEB areas and open areas, indicating that the impacts of solar radiation and wind or the lack of them on pedestrian's thermal comfort perceptions have not been well predicted by the three models. These investigations, on the one hand, characterize the benefits that elevated building designs have on the pedestrian-level microclimate and provide references and inspirations for urban planners to enhance pedestrian thermal comfort by altering building designs; on the other hand, indicate the need to refine the thermal comfort models for better outdoor thermal comfort assessment.
•Underneath-elevated-building and open areas mainly differ in radiation and wind.•Area underneath elevated building is significantly more comfortable in hot weather.•PET, UTCI and UC-Berkeley outputs correlate well with Mean Thermal Sensation Vote.•Extra considerations of radiation and wind are required to improve model predictions.
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In recent years, walkability is increasingly integrated into sustainability strategies, considering its many health and environmental benefits. Besides, thermal comfort also has been progressively ...promoted as a critical measure for pedestrian comfort and wellbeing. Despite the relevance of the two concepts, few studies combined them in a comprehensive model. This study considers thermal comfort in assessing walkability by developing a new measurement tool, the Street Walkability and Thermal Comfort Index (SWTCI), which focuses on comfort facilities and Physiological Equivalent Temperature (PET), at the street scale. The applied point system method requires combining a questionnaire survey, observations, and in situ measurements (air temperature, wind velocity, and relative humidity). The questionnaire survey (330 responders) measured 21 street design indicators' importance, using a five-point Likert scale ranging from 1 (least important) to 5 (very important). The observation technique seeks to evaluate every pedestrian comfort indicator score (Sis). The in situ measurements permit Envi-met's calibrated data validation and getting the mean radian temperature (Tmrt). Those were considered in the PET's calculation using Rayman software. Three distinct streets have been chosen in Annaba city, Algeria, within the Mediterranean climate (Csa). The results show that the SWTCI achieves its highest score on the three streets when the thermal perception is neutral (20 < PET <26), and its lowest score, with a warm thermal sensation (28 < PET < 31). Despite the divergence in PET values, the highest score of SWTCI was 33%, reflecting a low comfort quality and minimal pedestrian facilities. Applying the SWTCI method can transform uncomfortable streets into an ideal walkable and pleasant path by finding the problems and proposing improvements.
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•SWTCI tool combines walkability and thermal comfort at the street scale.•Exploration of PET and pedestrian comfort facilities became a systematic method.•PET assessment is based on the calibration process in the Mediterranean climate.•PET comfort range has a considerable effect on the SWTCI average.•Applying this method can transform uncomfortable streets into ideal walkable paths.
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Comfortable outdoor environment benefits the health of citizens and reduces energy consumption and pollution. This study discusses different outdoor thermal sensation and comfort evaluation methods ...in severe cold area. The database was from a year-long outdoor thermal comfort survey conducted in Harbin, China. Thermal sensation evaluation was developed using meteorological parameters and three popular thermal comfort indices including Standard Effective Temperature (SET*), Physiologically Equivalent Temperature (PET) and Universal Thermal Climate Index (UTCI). Thermal comfort prediction was developed by the three thermal comfort indices and acceptability. Original thermal sensation scales of SET* and PET were less applicable to predict thermal sensation vote (TSV). Calibrated scales of the three indices were obtained based on linear regression results and probit analysis. The accuracies of calibrated scales of thermal sensation were all below 32.8%. The comfortable thermal sensation range in severe cold area varied from “slightly cool” to “hot”. This calibrated range improved accuracies of thermal comfort predicting by around 20%. The unacceptability appropriate to define comfortable range was 9% on the cold thermal sensation side and 26% on the hot side. Adaptation and local exposure also acted on thermal sensation and comfort apart from factors included in thermal comfort indices. Our results provide practical thermal sensation and thermal comfort scales for severe cold area. The discussions indicate the significance of considering adaptation and local exposure for further improving thermal sensation and comfort predicting.
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•Original scale of UTCI is superior to SET* and PET in thermal sensation evaluation.•Performance of obtained thermal sensation prediction methods show minor difference.•Comfortable range for adults in severe cold area is from “slightly cool” to “hot”.•Unacceptability to define comfortable range is 9% (cold side) and 26% (hot side).•Adaptation and local exposure are vital for further improving comfort predicting.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Urban green and blue infrastructures (GBI) are considered an effective tool for mitigating urban heat stress and improving human thermal comfort. Many studies have investigated the thermal effects of ...main GBI types, including trees, green roofs, vertical greenings, and water bodies. Their physical characteristics, planting designs, and the surrounding urban-fabric traits may impact the resultant thermal effects. ENVI-met, a holistic three-dimensional modeling software which can simulate the outdoor microclimate in high resolution, has become a principal GBI research tool. Using this tool, the GBI studies follow a three-step research workflow, i.e., modeling, validation, and scenario simulation. For providing a systematic and synoptic evaluation of the extant research workflow, a comprehensive review was conducted on GBI-targeted studies enlisting ENVI-met as the primary tool. The findings of 79 peer-reviewed studies were analyzed and synthesised for their modeling, validation, and scenario simulation process. Special attention was paid to scrutinising their data sources, evaluating indicator selection, examining main analytical approaches, and distilling recommendations to improve the research workflow. This review provides researchers with an overview of the ENVI-met methodology and recommendations to refine research on GBI thermal effects.
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•A two layered control strategy integrating DP and fuzzy PID is built.•PPTC can accurately describe the thermal habit of passenger.•DP integrates the PPTC, VVP and WIR and achieves a low energy ...cost.•Fuzzy PID well responses the requirement of refrigeration capacity from DP.•Two layered strategy raises comfort and control precision and saves energy.
A two-layered control strategy is proposed for the air conditioning (AC) systems of electric vehicles. Unlike traditional rule-based controllers such as the on–off controller and proportion-integral-derivative (PID) controller, this strategy includes a decision layer and a control strategy. The core algorithm in the decision layer is the dynamic programming (DP), which integrates information from the thermal habit predictor of the passenger, vehicle velocity planner, and weather information receiver. The DP optimises the development of the cabin temperature to minimise the energy consumption of the AC system and sends the planned temperature to the control layer. The control layer uses a fuzzy PID algorithm to adjust the compressor speed based on the planned temperature profile, such that the real-world cabin temperature approaches the planned temperature. This two-layered control strategy is applied to a car whose AC-cabin system was verified by test data, and the results are compared with those obtained by the on–off controller and PID. When the target cabin temperature is not manually adjusted, the energy cost of the proposed strategy is 28.2% and 5.4% lower than those of the on–off controller and PID, respectively, at the ambient temperature profile of Environment 1 (described herein), and its maximum fluctuation of the cabin temperature is 92.8% and 68.2% smaller than those of the on–off controller and PID, respectively. At the ambient temperature of Environment 2 (described herein, lower than that of Environment 1), the energy cost of the proposed strategy is 37.1% and 5.9% lower, and the maximum fluctuation of the cabin temperature is 96.8% and 86.4% smaller, compared to the on–off controller and PID, respectively. When the target temperature is repeatedly set for the on–off controller and PID (first to 20 °C, then to 24.3 °C), the AC system consumes extra energy, leading to poor thermal comfort. Because the proposed strategy automatically sets the cabin temperature to the temperature preferred by the passenger, there is no extra adjustment of the target and the thermal environment inside the cabin is optimal for the passenger. Under this condition, the developed strategy can produce energy savings of 30.2% and 12.4%, compared to the on–off strategy and PID, respectively. Thus, the two-layered strategy can control the cabin temperature precisely, provide the passenger with a good thermal environment, and produce energy savings for the AC system.
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Thermal comfort markedly impacts our health, well-being and work productivity. This article is a review of practices generally adopted to quantify human thermal comfort in buildings. The review ...indicates that there is significant variation in comfort requirements due to diversified socio-cultural set-up and local adaptive behaviour. Thus, the localised thermal comfort models need to be developed to identify the actual comfort requirements helpful in drafting new local comfort standards. The study justifying the relationship between thermal comfort and indoor air quality are scant and need to be explored as such relationships are greatly dependent on occupant's adaptive behaviour. Further, the interdisciplinary research on thermal comfort which not only helps in real-time assessment but also covers other critical aspects like building architecture and energy consumption is lacking in the literature. Moreover, this review paves way for research on thermal comfort in countries where high building stock is expected in future.
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