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

Abstract

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.

Personal thermal comfort models with wearable sensors

“…The PMV model has its highest prediction accuracy in the thermal neutral zone and it decays towards the extremes of thermal sensation scale [16]. We showed that personal thermal comfort models using wearable sensors do exactly the opposite.…”

“…The models were trained on the entire dataset of all the 14 subjects instead of each subject and then averaged. In general, applying each individual feature or feature combinations generates a prediction power greater than the conventional PMV/adaptive model that has a very low prediction power on thermal preference (AUC≈0.5) [16,26].…”

“…Also, both models ignore aspects of human thermo-regulation and important personal psychophysics influencing the perception of thermal comfort [14,15]. The PMV predicts thermal sensation correctly only one out of three times and has a mean absolute error of one unit on the thermal sensation scale [16]. The main limitation of both PMV and adaptive models is that these two models were developed based on aggregated data from a large population.…”

Season influence on rapid thermal sensation assessment by young adults

“…Calculations of thermal sensations are commonly done by Fanger's energy balance models. 13 However, more recent experimental studies have shown that observed and calculated values might differ considerably [14][15][16][17][18] leading to the development of theories explaining the differences according to factors such as "short-term thermal adaptation" and "thermal expectation." 19 Additionally, thermal sensation of females and males in the same thermal environment, activity, and clothing insulation has been reported as being consistently different.…”

“…Calculations of thermal sensations are commonly done by Fanger's energy balance models . However, more recent experimental studies have shown that observed and calculated values might differ considerably leading to the development of theories explaining the differences according to factors such as “short‐term thermal adaptation” and “thermal expectation.”…”