Predicted Mean Vote Thermal Comfort Research Articles

Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning

A wide range of techniques exist to help control the thermal comfort of an occupant in indoor environments. A novel technique is presented here to adaptively estimate the occupant’s metabolic rate. This is performed by utilising occupant’s actions using computer vision system to identify the activity of an occupant. Recognized actions are then translated into metabolic rates. The widely used Predicted Mean Vote (PMV) thermal comfort index is computed using the adaptivey estimated metabolic rate value. The PMV is then used as an input to a fuzzy control system. The performance of the proposed system is evaluated using simulations of various activities. The integration of PMV thermal comfort index and action recognition system gives the opportunity to adaptively control occupant’s thermal comfort without the need to attach a sensor on an occupant all the time. The obtained results are compared with the results for the case of using one or two fixed metabolic rates. The included results appear to show improved performance, even in the presence of errors in the action recognition system.

Optimization modeling method for coal-to-electricity heating load considering differential decisions

Heating by electricity rather than coal is considered one effective way to reduce environmental problems. Thus, the electric heating load is growing rapidly, which may cause undesired problems in distribution grids because of the randomness and dispersed integration of the load. However, the electric heating load may also function as an energy storage system with optimal operational control. Therefore, the optimal modeling of electric heating load characteristics, considering its randomness, is important for grid planning and construction. In this study, the heating loads of distributed residential users in a certain area are modeled based on the Fanger thermal comfort equation and the predicted mean vote thermal comfort index calculation method. Different temperatures are considered while modeling the users’ heating loads. The heat load demand curve is estimated according to the time-varying equation of interior temperature. A multi-objective optimization model for the electric heating load with heat energy storage is then studied considering the demand response (DR), which optimizes economy and the comfort index. A fuzzy decision method is proposed, considering the factors influencing DR behavior. Finally, the validity of the proposed model is verified by simulations. The results show that the proposed model performs better than the traditional method.

The characteristics of the patient’s requirement for the ward thermal environment

In order to create and evaluate the thermal environment in hospital wards, field experiments, including subjective surveys and field thermal environment tests, were conducted in 4 hospitals. A total of 778 effective data sets were obtained. The Predicted Mean Vote (PMV) of the seated patients were calculated according to Fanger’s PMV thermal comfort model, and the PMV of the lying patients were calculated based on Lin & Deng’s PMV thermal comfort model in sleeping environments. The thermal insulation values of bedding systems were calculated according to the heat transfer equation, and their health status were described and quantified by the EQ-5D health index. The characteristics of the patient’s requirements of the thermal environment were analyzed, including neutral temperature, preferred temperature, 80% acceptable temperature etc. The logistic regression model of binomial classification shows that operative temperature, EQ-5D index, ward department and activity level had significant influence on the difference patients’ thermal sensation.

Sensitivity study for the PMV thermal comfort model and the use of wearable devices biometric data for metabolic rate estimation

This paper studies the sensitivity of the Predicted Mean Vote (PMV) thermal comfort model relative to its environmental and personal parameters of a group of people in a space. PMV model equations, adapted in ASHRAE Standard 55–Thermal Environmental Conditions for Human Occupancy, are used in this investigation to conduct parametric study by generating and analyzing multi-dimensional comfort zone plots. It is found that personal parameters such as metabolic rate and clothing have the highest impact. However, as these parameters are difficult to estimate or measure, they are usually assumed to be default values (rest conditions and light clothing). In this work, we show the application of the human-in-the-loop sensor data of wearable devices to provide a continuous feedback for the averaged metabolism value of building occupants to be used in the PMV calculation. Moreover, we motivate the use of these sensor data to develop a new personalized comfort model.

Revisiting Fanger’s thermal comfort model using mean blood pressure as a bio-marker: An experimental investigation

Fanger’s heat balance model led to the formulation of the predicted mean vote (PMV) and predicted percentage dissatisfied (PPD) for rating thermal comfort in buildings. The authenticity of PMV/PPD model has been questioned by several studies, thus opening the debate for revisiting the main parameters in PMV thermal comfort model. In this experimental study, a correlation between biomarker ‘mean blood pressure (MAP)’ and the ‘activity level’, was developed to improve the thermal prediction of PMV model. This study revealed a strong correlation between mean blood pressure and the activity level with a confidence level of 96%. Field assessments of PMV model were conducted in air conditioned as well as naturally ventilated buildings to analyze the effect of mean blood pressure on the PMV model. In air-conditioned buildings, PMV model overestimated the thermal sensation up to 54% as compared to actual vote, whereas the overestimation of modified model (mPMV) was found to be 22% only. The PPD deviations of mPMV and PMV models were found to be 8% and 28% respectively. Statistical analysis on the collected data strengthened the significance of mPMV on PMV model. In naturally ventilated buildings, the correlation found to be insignificant due to uncontrolled variables.