Development of the ASHRAE Global Thermal Comfort Database II

Ličina, Veronika Földváry; Cheung, Toby; Zhang, Hui; Dear, Richard de; Parkinson, Thomas; Arens, Edward; Chun, Chungyoon; Schiavon, Stefano; Luo, Maohui; Brager, Gail; Li, Peixian; Kaam, Soazig; Adebamowo, M.; Andamon, M; Babich, Francesco; Bouden, Chiheb; Bukovianská, Hana Pustayová; Cândido, Christhina; Cao, Bin; Carlucci, Salvatore; Cheong, David; Choi, Joon Ho; Cook, Malcolm; Cropper, Paul C.; Deuble, Max; Heidari, Shahin; Indraganti, Madhavi; Jin, Quan; Kim, Hyojin; Kim, Jungsoo; Konis, Kyle; Singh, Manoj Kumar; Kwok, Alison G.; Lamberts, Roberto; Loveday, Dennis L.; Langevin, Jared; Manu, Sanyogita; Moosmann, Cornelia; Nicol, Fergus; Ooka, Ryozo; Oseland, Nigel; Pagliano, Lorenzo; Petráš, Dušan; Rawal, Rajan; Romero, Ramona; Rijal, Hom Bahadur; Sekhar, Chandra; Schweiker, Marcel; Tartarini, Federico; Tanabe, Shin‐ichi; Tham, Kwok Wai; Teli, Despoina; Toftum, Jørn; Toledo, Linda; Tsuzuki, Kazuyo; Vecchi, Renata De; Wagner, Andreas; Wang, Zhaojun; Wallbaum, Holger; Webb, Lauren M.; Yang, Linlin; Zhu, Yingxin; Zhai, Yongchao; Zhang, Yufeng; Zhou, Xiang

doi:10.1016/j.buildenv.2018.06.022

Cited by 293 publications

(65 citation statements)

References 39 publications

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“…The plots in Figure 4 show the overall thermal sensation and preference (sorted by the percentage of "No change") for each subject. Consistent to a larger scale meta-data (the Comfort Database [70]), the median thermal sensations for all participants are within the thermal neutrality (-0.5< TS < 0.5) except for the subject (ID = 5, TSmedian = 0.7) and the subject (ID = 6, TSmedian = -0.6). However, the range of thermal sensations varies extensively among them.…”

Section: Thermal Sensation and Preferencesupporting

confidence: 80%

“…We observed in the Comfort Database [70] that thermal preference is strongly correlated with thermal sensation only when people are outside the thermal neutrality. In specific, the Pearson correlation between thermal sensation and thermal preference was only -0.06 within the thermal neutrality (-0.5 to 0.5) as compared to -0.67 when thermal sensation was not neutral.…”

Section: Comparison Of Prediction Power For Different Thermal Sensatimentioning

confidence: 82%

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Personal thermal comfort models with wearable sensors

Liu

Schiavon

Das

et al. 2019

Building and Environment

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188

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A personal comfort model is an approach to thermal comfort modeling, for thermal environmental design and control, that predicts an individual's thermal comfort response, instead of the average response of a large population. We developed personal thermal comfort models using lab grade wearable in normal daily activities. We collected physiological signals (e.g., skin temperature, heart rate) of 14 subjects (6 female and 8 male adults) and environmental parameters (e.g., air temperature, relative humidity) for 2-4 weeks (at least 20 hours per day). Then we trained 14 models for each subject with different machine-learning algorithms to predict their thermal preference. The results show that the median prediction power could be up to 24% /78% /0.79 (Cohen's kappa/accuracy/AUC) with all features considered. The median prediction power reaches 21% /71% /0.7 after 200 subjective votes. We explored the importance of different features on the prediction performance by considering all subjects in one dataset. When all features included for the entire dataset, personal comfort models can generate the highest performance of 35% /76% /0.80 by the most predictive algorithm. Personal comfort models display the highest prediction power when occupants' thermal sensations is outside thermal neutrality. Skin temperature measured at the ankle is more predictive than measured at the wrist. We suggest that Cohen's kappa or AUC should be employed to assess the performance of personal thermal comfort models for imbalanced datasets due to the capacity to exclude random success.

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Section: Thermal Sensation and Preferencesupporting

confidence: 80%

Section: Comparison Of Prediction Power For Different Thermal Sensatimentioning

confidence: 82%

Personal thermal comfort models with wearable sensors

Liu

Schiavon

Das

et al. 2019

Building and Environment

Self Cite

188

View full text Add to dashboard Cite

show abstract

“…Humphreys just on the discrepancy between PMV and observed thermal sensation and the scope of discussion was only available between AC and NV buildings [6]. The recently published ASHRAE Global Thermal Comfort Database II [35], referred to as the Comfort Database hereafter) contains approximately 107,000 records from more than 50 field studies around the world and represents the largest thermal comfort database of its kind. We need to reevaluate the reliability of the PMV-PPD model across multiple contexts (building types, ventilation strategies and climates) using this new resource.…”

Section: Objectivesmentioning

confidence: 99%

Analysis of the accuracy on PMV – PPD model using the ASHRAE Global Thermal Comfort Database II

Cheung

Schiavon

Parkinson

et al. 2019

Building and Environment

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308

114

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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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“…There is no similar study yet focusing on the case of Colombia. The adaptive model was developed and recently updated using information from two databases: the ASHRAE RP-884 database published in 1998 comprising 23 field research projects [20] and the ASHRAE Global Thermal Comfort Database II published in 2018 comprising 42 field research projects [33,34]. A closer look at these databases evidences their limited amount of information from tropical regions and climates of Africa, South America, Central America, and the Caribbean.…”

Section: Thermal Comfort Assessment Modelsmentioning

confidence: 99%

“…ASHRAE (American Society of Heating Engineers, Refrigeration and Air Conditioning) databases (I + II) according to projects location and climate, based on information from[20,33,35].…”

mentioning

confidence: 99%

The Importance of Standardised Data-Collection Methods in the Improvement of Thermal Comfort Assessment Models for Developing Countries in the Tropics

et al. 2019

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Thermal comfort in the built environment is one of the most defining parameters influencing energy use, environmental quality, and occupant satisfaction. Unfortunately, there is a lack of research in this area within developing countries, which are becoming increasingly urbanised and where mechanical air conditioning demands are rising. Many of these countries are adopting thermal comfort standards such as the ASHRAE Standard 55, the EN 15251, and the ISO 7730 to regulate the use of air-conditioning; even when these standards have been widely criticised for their inadequacy within geographical regions different to the ones that they were designed for. Research suggests the need to confirm these models through further post-occupancy studies and fieldwork. Deficiencies in data collection and methodologies are thought to require particular attention to develop algorithms that can predict thermal comfort levels accurately. Comprehensive strategies considering interrelated psychological, physiological and social factors are needed. This manuscript highlights gaps of research, specifically within tropical developing countries, through the analysis of Colombia as a case study. It emphasises the importance of standardised fieldwork data and gives examples of alternative collection systems. This aims to contribute to the understanding of occupant´s adaptive behaviours and their impact on the mitigation of climate change.

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Development of the ASHRAE Global Thermal Comfort Database II

Cited by 293 publications

References 39 publications

Personal thermal comfort models with wearable sensors

Personal thermal comfort models with wearable sensors

Analysis of the accuracy on PMV – PPD model using the ASHRAE Global Thermal Comfort Database II

The Importance of Standardised Data-Collection Methods in the Improvement of Thermal Comfort Assessment Models for Developing Countries in the Tropics

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