Assessing user thermal sensation in the Aegean region against standards

Çalış, Gülben; Kuru, Merve

doi:10.1016/j.scs.2016.11.013

Cited by 24 publications

(12 citation statements)

References 15 publications

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“…Despite the mean discrepancy between PMV and OTS being close to zero at neutral votes in Figure 1c, the prediction accuracy of PMV at neutral remains less than half in most cases regardless of ventilation, building and climate types (cases 2 to 11). Surprisingly, in all climates where data was available (cases 12 & 15, 21 & 24 and 30 & 33), we found a higher PMV prediction accuracy for neutrality in NV compared to AC offices.…”

Section: Pmv Prediction Accuracymentioning

confidence: 62%

“…The first method graphs the linear relationship between the predicted (PMV or PPD) and observed (thermal sensation or percent of dissatisfied) votes. With this method, the model performance improves as the slope of the regression approaches 1 and the intercept nears 0 [11][12][13]. Along with the slope and intercept, the coefficient of determination (r 2 ) and the error terms between the predicted and observed values are used as metrics of model accuracy.…”

Section: Validation Methods and Accuracy Of The Pmv-ppd Modelmentioning

confidence: 99%

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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

325

117

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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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Section: Pmv Prediction Accuracymentioning

confidence: 62%

Section: Validation Methods and Accuracy Of The Pmv-ppd Modelmentioning

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

325

117

View full text Add to dashboard Cite

show abstract

“…In practice, the equation calculated represents the way the occupants of the building actually feel compared with the sensation that was theoretically expected given the indoor monitored conditions. Using such an equation to model the relationship between OMV and AMV values is very common in the literature [26][27][28] as the examined variables appear to be well fitted by linear models displaying high values of coefficient of determination. This linear effect can be also observed in Fig.…”

Section: Fig 3 Generalized Feedback Duration and Effectmentioning

confidence: 99%

Decision Support for Intelligent Energy Management in Buildings Using the Thermal Comfort Model

Marinakis¹,

Doukas²,

Spiliotis³

et al. 2017

IJCIS

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The main objective of this paper is to present a transparent Decision Support System (DSS) for the energy managers of buildings, which can assist them in setting indoor temperature set point, based on the feedback received by the occupants. Within the proposed DSS, the Thermal Comfort Validator (TCV) tool is introduced, a fully responsive and cross-platform web-app which exploits the Predicted Mean Vote comfort theory by considering real-time feedback of the occupants. The TCV facilitates the detection of the range of accepted temperature inside a building, by correlating "real" with "predicted" thermal comfort, to overcome the limits of the standardized approaches. The proposed system can reveal an important potential for achieving energy savings by means of dynamic event driven data collection and processing, while ensuring high levels of comfort.

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“…Since the PMV-PPD model recommends that a narrow temperature range be applied equally across all building types, climatic zones and population, the prediction accuracy of PMV and PPD indices has been questioned by many researchers. Studies conducted in hot and humid climatic conditions prove that the PMV-PPD model tends to over-predict the perceived warmth in the built environment [3][4][5][6]. Furthermore, thermal comfort complaints have been reported in different types of buildings [3,[7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Do Thermal Comfort Standards Ensure Occupant Satisfaction? Learning From Occupants’ Thermal Complaints

Kocaman¹,

Kuru²,

Çalış³

2019

Proceedings of the Creative Construction Conference 2019

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Today, buildings are operated according to the standards (i.e. thermal), however; the recommended values in the standards might not necessarily address occupants' needs, and, thus, occupant complaints might arise. This study aims at assessing the performance of the predicted mean vote (PMV) model to detect occupant thermal dissatisfaction. The case study was conducted in a commercial building located in Paris, France between January 2017 and May 2018. Indoor environmental conditions were monitored via sensors and an online tool was used to collect occupant thermal complaints. A total of 53 thermal complaints were analyzed and the corresponding measurements were checked against the reference values suggested by the ISO 7730 Thermal Comfort Standard. The results show that all of the operative temperature measurements both in the heating and cooling seasons were within the thresholds suggested by the standards. In addition, the PMV method suggested that only 4% of the occupants were dissatisfied with the indoor environment. However; the actual dissatisfaction ratio of occupants was 100% under these indoor environmental conditions. The findings of this study show that predefined comfort ranges, and, thus thermal comfort standards are not able to predict occupant thermal dissatisfaction.

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Assessing user thermal sensation in the Aegean region against standards

Cited by 24 publications

References 15 publications

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

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

Decision Support for Intelligent Energy Management in Buildings Using the Thermal Comfort Model

Do Thermal Comfort Standards Ensure Occupant Satisfaction? Learning From Occupants’ Thermal Complaints

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