Adjustments of the adaptive thermal comfort model based on the running mean outdoor temperature for Chinese people: A case study in Changsha China

202

In recent years, adaptive thermal comfort models have been integrated into several building design and operations regulatory documents. Although the theoretical background of the adaptive thermal comfort models is quite mature, still some ambiguities exist for their application. The objective of this study is to identify the main sources of uncertainty around application of adaptive models and to analyze quantitatively the difference between the adaptive comfort models proposed by the regulatory documents when applied across a spectrum of different climate zones. This paper analyzes the adaptive models in ASHRAE Standard 55, the European EN 15251 (and its revision prEN 16798), the Dutch ISSO 74 and the Chinese GB/T 50785. For each regulatory document, the major variations or sources of uncertainty are investigated: for ASHRAE 55, the length of the calculation period of the prevailing mean of outdoor temperature, and for EN 15251, prEN 16798, and GB/T 50785, the exponential decay weighting factors used in the calculation of the running mean outdoor temperature. This study shows that, although these regulatory documents have promoted the uptake of adaptive comfort models by practitioners and designers, uncertainties surrounding their application obstruct full exploitation. In response, this paper offers a fine-tuning of some of the adaptive comfort models. However, the issue of adaptive models' applicability in hybrid ventilation or mixed-mode buildings is still to be resolved, as is a rational basis for identifying the operational mode of such buildings when the adaptive models can be applied, because of their intermittent compliance during transition seasons and also extreme weather events.

Section: The Optimal Adaptive Comfort Temperaturessupporting

confidence: 91%

“…caused by the completely different climate conditions between European countries and China" [47]. But there is not a tentative explanation for the cold-bias during the summer months.…”

Section: The Optimal Adaptive Comfort Temperaturesmentioning

confidence: 98%

Section: The Optimal Adaptive Comfort Temperaturesmentioning

confidence: 99%

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Review of adaptive thermal comfort models in built environmental regulatory documents

Carlucci

Bai

Dear

et al. 2018

202

“…4 surveys, with considerations given to people's behavioral adjustments, physiological acclimatization, and psychological adaptations [18]. Extensive on-site studies have been conducted to enrich the adaptive thermal comfort in different climatic zones [2,7,13,15,[21][22][23][24][25][26][27][28][29]. Yan et al [30] conducted a year-round field investigation in different climatic regions in eastern China and proposed an individual thermal adaptive model for naturally ventilated environments.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Effects of indoor humidity on building occupants’ thermal comfort and evidence in terms of climate adaptation

Kong

Hong

et al. 2019

Similar as temperature, air humidity may affect people's thermal comfort and humidity adaptation may happen when people changing their living conditions. To provide evidence on both effect from humidity on people's thermal comfort and their humidity adaptation, a comparative study has been conducted in a controlled climate chamber. During the experiment, the air temperature was set as 25 °C and 28 °C respectively and the relative humidity was changing between 20% and 90%. There were twenty four participants involved in this experiment, with half living in High Humidity (HH) regions of China, such as Chongqing, for over 20 years, and another half recently moved to Chongqing from Low Humidity (LH) regions in northwestern China. During the experiment, mean skin temperature was measured as objective an important parameter and subjective questionnaires were used to subjectively collect

“…is the mean indoor temperature in °C, and is the assumed regression gradient, also called Griffiths coefficient, in /°C. This method has been used in many field studies all over the world to derive neutral temperatures [28][29][30][31][32][33][34][35][36], including the derivation of the European adaptive thermal comfort model [37]. This method has been deemed useful when it is difficult to reach statistically significant linear regressions, due to, for example, small sample sizes, low variance of the indoor temperature, or non-linearly dependent data with interaction effects.…”

Section: Wherementioning

confidence: 99%

The influence of relative humidity on adaptive thermal comfort

Vellei

Herrera

Fosas

et al. 2017

138

Buildings generate nearly 30% of global carbon emissions, primarily due to the need to heat or cool them to meet acceptable indoor temperatures. In the last 20 years, the empirically derived adaptive model of thermal comfort has emerged as a powerful alternative to fixed set point driven design. However, current adaptive standards offer a simple linear relationship between the outdoor temperature and the indoor comfort temperature, assumed to sufficiently explain the effect of all other variables, e.g. relative humidity (RH) and air velocity. The lack of a signal for RH, is particularly surprising given its well-known impact on comfort. Attempts in the literature to either explain the lack of such a signal or demonstrate its existence, remain scattered, unsubstantiated and localised. In this paper we demonstrate, for the first time, that a humidity signal exists in adaptive thermal comfort using global data to form two separate lines of evidence: a meta-analysis of summary data from 63 field studies and detailed field data from 39 naturally ventilated buildings over 8 climate types. We implicate method selection in previous work as the likely cause of failure to detect this signal, by demonstrating that our chosen method has a 56% lower error rate. We derive a new designer-friendly RH-inclusive adaptive model that significantly extends the range of Highlights • The influence of relative humidity on adaptive thermal comfort explained. • A new adaptive thermal comfort model which considers the effect of relative humidity introduced. • The current model is shown to overestimate overheating by 30% over 13 global locations.