Elsevier

Energy and Buildings

Volume 47, April 2012, Pages 312-320
Energy and Buildings

A method to weight three categories of adaptive thermal comfort

https://doi.org/10.1016/j.enbuild.2011.12.007Get rights and content

Abstract

The adaptive thermal comfort theory considers people as active rather than passive recipients in response to ambient physical thermal stimuli, in contrast with conventional, heat-balance-based, thermal comfort theory. Occupants actively interact with the environments they occupy by means of utilizing adaptations in terms of physiological, behavioural and psychological dimensions to achieve ‘real world’ thermal comfort. This paper introduces a method of quantifying the physiological, behavioural and psychological portions of the adaptation process by using the analytic hierarchy process (AHP) based on the case studies conducted in the UK and China. Apart from three categories of adaptations which are viewed as criteria, six possible alternatives are considered: physiological indices/health status, the indoor environment, the outdoor environment, personal physical factors, environmental control and thermal expectation. With the AHP technique, all the above-mentioned criteria, factors and corresponding elements are arranged in a hierarchy tree and quantified by using a series of pair-wise judgements. A sensitivity analysis is carried out to improve the quality of these results. The proposed quantitative weighting method provides researchers with opportunities to better understand the adaptive mechanisms and reveal the significance of each category for the achievement of adaptive thermal comfort.

Highlights

▸ Evaluation of the weight of thermal comfort adaptation factors using AHP method. ▸ A hierarchy structure framework of adaptive thermal comfort elements and their interactive relationships. ▸ Physiological adaptation plays the most significant role to thermal comfort. ▸ Cultural, habituation and socioeconomic issues affect psychological and behavioural thermal adaptation.

Introduction

The conventional human heat-balance based thermal comfort theory mainly focuses on well-controlled environments and it has limitation in assessing the environment that occupants are free to adapt [1], [2]. In contrast, the concept of adaptive thermal comfort acknowledges the thermal adaptations of occupants in the built environment and classifies them as: physiological adjustment, behavioural adaptation and psychological adjustment, respectively [3].

The adaptive thermal comfort theory which is based on the data gathered from field studies has been acknowledged by many researchers [1], [3], [4] and related organizations [5], [6], particularly in the context of thermal comfort assessment within a non-air-conditioned space. According to the adaptive thermal comfort theory, the thermal comfort achieved by occupants in a non-air-conditioned environment is regarded as the result of the combined effects of ambient physical environmental stimuli and non-physical issues, such as personal life-styles, cultural issues, socio-economic factors, etc. After recognizing the limitations of the conventional PMV–PPD (predicted mean vote–predicted percentage of dissatisfied) index, Fanger and co-worker [7] modified his PMV model by introducing an expectancy factor e, which was varying between 0.5 and 1.0 to correct the results that apply to non-air-conditioned buildings. Subsequently, in acknowledgment of the discrepancies in the predicted and observed thermal sensations, the adaptive thermal model of thermal comfort for naturally ventilated buildings was first introduced in the ASHRAE 55-2004 [5] Standard by relating the indoor operative temperature to the mean monthly outdoor air temperature. The Charted Institution of Building Services Engineers (CIBSE) standard, CIBSE Guide A [8], also acknowledged and included the adaptive thermal comfort approach and presented the corresponding adaptive thermal comfort temperature ranges based on the outdoor running mean temperature for offices in both free running buildings and heated or cooled buildings. Therefore, the thermal comfort mentioned in the above literature is no longer determined by a specific fixed set-up temperature or the combination of several physical environmental variables, but by a broad dynamic range varying with local outdoor climate and being influenced by non-physical factors in a free-running environment. Thus, the thermal comfort achieved by occupants in the real dynamic environment should be an outcome of active adaptations which were influenced by multiple factors [9], [10].

Gagge et al. [11] carried out a series of experiments with unclothed sedentary subjects in the 1960s and described the physiological response over the range of ambient temperatures 12–48 °C. In his studies, shivering was an effective physiological way of offsetting cold temperatures since the resting metabolism was doubled and even sometimes tripled by shivering; in the extreme conditions of an air temperature at 48 °C, sweating contributed to a cooling effect of as much as 110 kcal/(m2 h); the skin temperature increased with the ambient air temperature until the latter reached 28 °C, after which skin temperature became fairly uniform. Yao et al. [12] recruited 20 young persons as subjects and carried out the experimental study regarding the physiological responses under different ambient temperatures in a climate chamber. They found that mean skin temperature would vary in response to changes in the ambient temperature. The ratio of low frequency power to high frequency power of the electrocardiograph (ECG) and the global relative power of the different electroencephalogram (EEG) frequency bands were sensitive to the ambient temperatures as well. Chatonnet and Cabanac [13] stated that ‘behavioural thermoregulation is well-developed in man and becomes preponderant and tends to supplant other forms of thermoregulation’. Humphreys [4] and Nicol et al. [14] attributed the broad comfort temperature range in an office environment in north-west Pakistan partly to the flexibility of traditional Pakistani clothing. Nicol and Raja [15] stated that the posture of the human body could be an effective behavioural adaptation since adopting an appropriate posture reduced valid clothing insulation and increased the effective body surface area for sweating. In addition, people adjust themselves to adapting to their environment and become accustomed to utilizing environmental controls to satisfy themselves. McIntyre [16] was one of the pioneers studying the role of expectation in thermal comfort, and stated that ‘a person's reaction to a temperature which is less than perfect will depend very much on his expectations, personality and what else he is doing at the time’. Brager et al. [17] performed an investigation in the San Francisco Bay area in which people with high degrees of control produced a higher comfort temperature in warm conditions compared with those having a low degree of control, and such temperature was more close to the temperature actually experienced.

The existing research into adaptive thermal comfort studies mainly focuses on the positive effects of a single component or a pooled effect. However, besides giving a statistical approximation of the general effect of such adaptive processes on the thermal perception vote, little is known about the individual contributions of the three types of adaptive processes to this effect [18], [19]. It is difficult to attribute the thermal comfort status achieved by an occupant to a certain category of adaptation because of the complexity of the interactive relationship among these various adaptations. Yao et al. [9] developed a theoretical adaptive predicted mean vote (aPMV) model based on the ‘Black Box’ theory, which reveals the interrelationship between the static heat balance and psychological and behavioural effects through theoretical analysis. Similar to the steady state, physiological adaptation is contained within a ‘Black Box’, but psychological and behavioural stimuli give an ‘adaptive (negative) feedback’. The aPMV model reveals the generic relationship of the adaptive predicted mean vote (aPMV) and the predicted mean vote (PMV) in free-running buildings [9]. However, the weighting of the effects of the three categories within the ‘Black Box’ remains unknown. It is commonly well-accepted that three categories of adaptation play role in enabling people to restore their thermal comfort status under various thermal environments, but the extent to which each category of adaptation contributes to achieving thermal comfort is still unknown. There is a need to gain knowledge of the weight of the contribution of each of the three categories to adaptive thermal comfort sensation in order to extend the static heat balance model to broader adaptive perspectives. This knowledge will help to bridge the gap between the steady state heat-balance-based and statistical field-studies-based approaches. In building energy management systems, to fully understand the heat balance and regulatory mechanism of human bodies, as well as people's psychological and behavioural adaptations, is essential for setting up facilities management and control strategies.

The aim of this paper is to develop a quantitative method which can identify the significance of these three categories in order to provide improved control strategies for building designers and facilities managers in the context of achieving thermal comfort and energy efficiency. The group analytic hierarchy process (AHP) has been applied to surveys from cases in the UK and China, respectively, to determine the weights of the three categories.

Section snippets

The adaptive thermal comfort

The adaptive comfort theory was first proposed in the 1970s due to the oil-shocks [3]. The adaptive principle is explained as: if a change occurs such as to produce discomfort, people react in ways which tend to restore their comfort [2]. In a real environment, people utilize various adaptive approaches freely according to their own thermal preference to achieve thermal comfort. Humphreys defined adaptive approaches as ‘The adaptive approach notices that people use numerous strategies to

The analytic hierarchy process

The AHP developed by Saaty [35] is a powerful and flexible, multi-criteria, decision-making tool designed to address complex problems where both quantitative and qualitative dimensions need to be taken into account [36], [37]. The feature of this methodology lies in its ability to decompose a complex multi-criteria problem into a hierarchical structure [35]. By pair-wise comparisons of the alternatives, AHP incorporates all the participants’ evaluations into a final decision. The scale which

The analytic hierarchy process analysis

In order to apply AHP analysis, an identical survey was carried out in 2011 in the UK and China, respectively. The purpose of selecting sites with different climatic characteristics, habituations, cultural backgrounds and socioeconomic conditions is to test if these factors are significant. After all, these non-physical factors will also affect the thermal comfort of occupants, particularly in a non-air-conditioned environment. The respondents in both places are experts and academics who have

Discussion

The results of the AHP method were based on subjective data collected via a questionnaire survey, they were representative as long as the consistency test had been passed. Such subjective information could be regarded as a comprehensive reflection considering the occupants’ living conditions, cultural issues, socio-economic factors, etc. For instance, it was identified that, in the China case, the importance of behavioural adaptation was regarded slightly higher than psychological adaptation;

Sensitivity analysis

According to the hierarchical tree used in this study (see Fig. 1), there are both objective and subjective issues concerned. Consequently, the priority given to the criteria weights is formed and influenced by those issues, particularly by subjective issues. Since the subjective assessments may vary among participants due to cultural, social and experiential differences the quality of the results requires careful consideration. In order to verify the reliability of the results derived by this

Summary and conclusions

People's control over creating a comfortable thermal environment not only enhances the thermal acceptability and percentage satisfaction with the outcome but also affect the building energy consumption and greenhouse gas emissions. The thermal comfort of occupants in ‘real world’ is the result of comprehensive, multi-factorial effects concerning both physical and non-physical dimensions. Understanding the weights of factors which influence thermal comfort is essential to better understand these

Acknowledgements

The authors would like to thank all respondents who participated in this study in the UK and China. Special thanks go to the Faculty of Urban Construction and Environmental Engineering, Chongqing University, China with the finance support from the National Natural Science foundation of China (50838009) for carrying out the field survey in Chongqing.

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