There are several different models or indices that can be used to assess thermal comfort conditions indoors as described below.
PMV/PPD method The PMV/PPD model was developed by
P.O. Fanger using heat-balance equations and empirical studies about
skin temperature to define comfort. Standard thermal comfort surveys ask subjects about their thermal sensation on a seven-point scale from cold (−3) to hot (+3). Fanger's equations are used to calculate the predicted mean vote (PMV) of a group of subjects for a particular combination of
air temperature,
mean radiant temperature,
relative humidity, air speed, metabolic rate, and
clothing insulation. ASHRAE Standard 55-2017 uses the PMV model to set the requirements for indoor thermal conditions. It requires that at least 80% of the occupants be satisfied. The PMV/PPD model has a low prediction accuracy. Using the world largest thermal comfort field survey database, the accuracy of PMV in predicting occupant's thermal sensation was only 34%, meaning that the thermal sensation is correctly predicted one out of three times. The PPD was overestimating subject's thermal unacceptability outside the thermal neutrality ranges (-1≤PMV≤1). The PMV/PPD accuracy varies strongly between ventilation strategies, building types and climates. Passive design improves thermal comfort in a building, thus reducing demand for heating or cooling. In many
developing countries, however, most occupants do not currently heat or cool, due to economic constraints, as well as climate conditions which border lines comfort conditions such as cold winter nights in Johannesburg (South Africa) or warm summer days in San Jose, Costa Rica. At the same time, as incomes rise, there is a strong tendency to introduce cooling and heating systems. If we recognize and reward passive design features that improve thermal comfort today, we diminish the risk of having to install HVAC systems in the future, or we at least ensure that such systems will be smaller and less frequently used. Or in case the heating or cooling system is not installed due to high cost, at least people should not suffer from discomfort indoors. To provide an example, in San Jose, Costa Rica, if a house were being designed with high level of glazing and small opening sizes, the internal temperature would easily rise above and natural ventilation would not be enough to remove the internal heat gains and solar gains. This is why Virtual Energy for Comfort is important.
World Bank's assessment tool the EDGE software (
Excellence in Design for Greater Efficiencies) illustrates the potential issues with discomfort in buildings and has created the concept of Virtual Energy for Comfort which provides for a way to present potential thermal discomfort. This approach is used to award for design solutions which improves thermal comfort even in a fully free running building. Despite the inclusion of requirements for overheating in CIBSE, overcooling has not been assessed. However, overcooling can be an issue, mainly in the developing world, for example in cities such as Lima (Peru), Bogota, and Delhi, where cooler indoor temperatures can occur frequently. This may be a new area for research and design guidance for reduction of discomfort.
Cooling Effect ASHRAE 55-2017 defines the Cooling Effect (CE) at elevated air speed (above ) as the value that, when subtracted from both the air temperature and the mean radiant temperature, yields the same SET value under still air (0.1 m/s) as in the first SET calculation under elevated air speed. it is also referred to as the Pierce Two-Node model. Its calculation is similar to PMV because it is a comprehensive comfort index based on heat-balance equations that incorporates the personal factors of clothing and metabolic rate. Its fundamental difference is it takes a two-node method to represent human physiology in measuring skin temperature and skin wettedness. Fountain and Huizenga (1997) developed a thermal sensation prediction tool that computes SET. The SET index can also be calculated using either the
CBE Thermal Comfort Tool for ASHRAE 55, ASHRAE Standard 55-2010 states that differences in recent thermal experiences, changes in clothing, availability of control options, and shifts in occupant expectations can change people's thermal responses. There are basically three categories of thermal adaptation, namely: behavioral, physiological, and psychological.
Psychological adaptation An individual's comfort level in a given environment may change and adapt over time due to psychological factors. Subjective perception of thermal comfort may be influenced by the memory of previous experiences. Habituation takes place when repeated exposure moderates future expectations, and responses to sensory input. This is an important factor in explaining the difference between field observations and PMV predictions (based on the static model) in naturally ventilated buildings. In these buildings, the relationship with the outdoor temperatures has been twice as strong as predicted.
Physiological adaptation The body has several thermal adjustment mechanisms to survive in drastic temperature environments. In a cold environment the body utilizes
vasoconstriction; which reduces blood flow to the skin, skin temperature and heat dissipation. In a warm environment,
vasodilation will increase blood flow to the skin, heat transport, and skin temperature and heat dissipation. If there is an imbalance despite the vasomotor adjustments listed above, in a warm environment sweat production will start and provide evaporative cooling. If this is insufficient,
hyperthermia will set in, body temperature may reach , and
heat stroke may occur. In a cold environment, shivering will start, involuntarily forcing the muscles to work and increasing the heat production by up to a factor of 10. If equilibrium is not restored,
hypothermia can set in, which can be fatal. Those occupants who take these sorts of actions tend to feel cooler at warmer temperatures than those who do not. The behavioral actions significantly influence energy simulation inputs, and researchers are developing behavior models to improve the accuracy of simulation results. For example, there are many window-opening models that have been developed to date, but there is no consensus over the factors that trigger window opening. People might adapt to seasonal heat by becoming more nocturnal, doing physical activity and even conducting business at night. ==Specificity and sensitivity==