Standard comfort measurements used to design buildings’ heating and cooling systems share a common flaw, according to new research.
The researchers said the findings could mean that designers have relied on inaccurate measurements for decades when building their systems.
In findings reported February 14 in the journal Scientific Reports, the researchers said the error was caused by the standard instrument used to measure temperature effects of radiant heating and cooling.
The instrument, called a globe thermometer, and associated formulas used to calculate comfort based on the sensor’s readings do not properly account for air flow called free convection.
In experiments, the failure led to temperature errors of more than two degrees Celsius, the researchers said.
Forrest Meggers, an assistant professor at Princeton University’s Andlinger Center for Energy and the Environment and one of the lead researchers, said the team uncovered problems with traditional measurements while building an outdoor exhibit in Singapore.
While the team had no issues keeping the exhibit participants comfortable using a radiant cooling system, using standard measurement techniques, the team had difficulty demonstrating these cooling effects.
Meggers, also an assistant professor of architecture, said designers take basic measurements with the globe thermometer and use formulas to determine how the radiant system affects occupant comfort levels in various environments.
Although the participants remained comfortable and the system was able to keep them feeling cool, the calculations showed the system was not working and that the environment should feel uncomfortable.
“That’s when we realized the formula was wrong. We had a hard time accepting it,” Meggers said.
The notion of using radiative heat exchange cooling or heating walls and surfaces to keep people nearby comfortable has been identified as an energy efficient design feature, but air conditioning is still the primary solution for keeping people comfortable in buildings in the United States and other places.
Radiant systems have not always been seen as effective.
The researchers say this miscalculation could help to explain why. Understanding and effectively capturing the impact of radiant systems on comfort can have a major impact on energy savings.
Letting the air reach five degrees warmer while cooling surfaces, the researchers say, can lower cooling demand by up to 40% and maintain occupant comfort.
People currently spend 87% of their time indoors [1]. As indoor activities are conducted more frequently, occupants are attempting to create indoor thermal environments that allow them to feel comfortable [2].
Fanger defined thermal comfort as “the condition of mind that expresses satisfaction with the thermal environment” [3].
Thus, the extent to which the thermal comfort of building occupants is maintained has become an important part of a building performance evaluation [4].
In addition, it is crucial to consider not only the thermal comfort of the occupants but also a management plan to satisfy the comfort level of an indoor environment during the design and operation of a building [5].
To create a comfortable indoor environment, most buildings use a set-point temperature control in the heating, ventilation, and air-conditioning (HVAC) systems, which measures the indoor temperature of a specific space and controls it through a comparison with the set-point temperature [6].
This control usually considers the indoor dry-bulb temperature for convenience, which is the set-point temperature set by the occupant or manager [7]. However, it is insufficient to consider the thermal equilibrium or radiant heat transfer of the human body inside a building [8].
To achieve thermal comfort of the occupants, it is important to consider not only the indoor dry-bulb temperature but also different variables, such as the thermal environment’s factors, building usage, occupant characteristics, and weather conditions; however, it is not easy to satisfy every occupant in a space and control the indoor conditions simultaneously [9,10].
In addition, occupants apply trial and error to set the indoor temperature to a comfortable level, thus causing occupant discomfort and leading to unnecessary energy consumption [11,12]. More notably, because different buildings have different shapes, types of insulation, fenestrations, and window-to-wall ratios, their thermal characteristics manifest in different ways.
Because the thermal characteristics of a building are a primary element, along with weather conditions, in establishing an indoor environment [5], both should be applied as key values in HVAC system controls. However, in the Republic of Korea, buildings are controlled using a one-size-fits-all model, pursuant to the laws and administrative regulations (26 ◦C for cooling; 20 ◦C for heating) to prevent excessive building energy consumption regardless of the thermal comfort of the occupants [13].
The different thermal characteristics of a building and daily changes in the weather conditions are not considered, and can potentially increase occupant discomfort and deteriorate the work productivity [6]. To overcome the limitations of conventional control, comfort controls have been studied by considering the indoor temperature and various factors of the thermal environment.
A previous study defined comfort control as “maintaining a constant level of comfort throughout the entire period” [14], and many studies have applied using various thermal environment indices, including the comfort zone developed by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) [8,15]; adaptive thermal comfort [16,17]; and Fanger’s predicted mean vote (PMV) model [11,12,18–23] as control criteria. The results indicate that applying comfort control is advantageous to increasing indoor comfort and reducing energy consumption, and a new direction for the progress of HVAC systems control was suggested.
However, a variety of problems have emerged in terms of comfort control; e.g., measurement sensors under difficult-to-measure variables, including the air velocity, mean radiant temperature (MRT), and clothing insulation; increased maintenance costs; and a delayed processing time owing to complex computations [24,25].
For these reasons, limitations in applying comfort control to actual buildings have been shown. Despite diverse methods being used to control HVAC systems for indoor environments, an incorrect set-point temperature may reduce the thermal comfort of the occupants and cause unnecessary energy consumption.
In addition, because the thermal characteristics of a building are diverse and dealing with outdoor environments is a daily challenge, a fixed and uniform set-point temperature is not always suitable for thermal comfort, and it is not easy for the occupants or manager to determine an optimal set-point temperature for a certain building. This study was motivated by the idea that an HVAC system should be controlled by considering the thermal characteristics of a building and the weather conditions.
Therefore, this study mainly focused on a derivation method of the optimal set-point temperature when considering daily changes in the weather conditions and the thermal characteristics of an office building using an HVAC system. To achieve an advanced thermal comfort in a controlled space, this study applied the operative temperature to the set-point temperature control instead of the dry-bulb temperature.
The operative temperature was defined as a uniform temperature of a radiantly black enclosure in which an occupant would exchange the same amount of heat by radiant and convection as in the actual non-uniform environment [26]. Additionally, it was dealt with as a thermal environment index that considers both the indoor temperature and the MRT, which have a significant effect on the PMV [27], among other major variables for a thermal environment.
Despite agreement among the current thermal comfort standards or thermal comfort models that the MRT must be considered in an HVAC system control, it has often been a practice to avoid measuring the MRT and instead assume that it is equal to the dry-bulb temperature [28,29], due to several reasons, such as complicated MRT measurement methods [30–32] and a hypothesis that surrounding indoor surfaces have a uniform temperature and radiation flux [33].
However, that can lead to the incorrect determination of PMV and comfort level [28]. Although recently, various attempts have emerged to predict variables such as metabolic rate and clothing insulation value [34–37], they are still limited when finding a way of predicting the MRT in high accuracy.
Therefore, instead of directly measuring the MRT with sensors and assuming that the MRT and drybulb temperature are equal, this study also proposes a prediction method for the MRT; namely, an MRT regression model that applies simple datasets, including the indoor thermal environment data of the subject building and the weather information at 3-h intervals provided from the Korea Meteorological Administration (KMA) [38].
From combining both an MRT regression equation and the operative temperature equation, the set-point temperature equation that can maintain an operative temperature suitable for the subject building was established and applied, such that, when the weather data are input, the set-point temperature suitable for the day is able to be changed for the HVAC system.
Despite the existence of other prediction methods (e.g., machine learning and artificial neural networks (ANNs)), the reason for using a regression analysis for MRT prediction is to create a set-point temperature equation that anyone can easily access while also using the operative temperature equation.
To achieve the above study objectives, this study concentrated on cooling control during summer (August) with high solar radiation. The equation used for accurately deriving the cooling set-point temperature was then developed by (i) establishing two types of datasets; namely, thermal environment and weather conditions (e.g., the indoor temperature, outdoor temperature, and sky cover) datasets;environment and weather conditions (e.g., the indoor temperature, outdoor temperature, and sky cover) datasets; (ii) deriving significant variables for predicting the MRT; (iii) constructing an MRT regression model using the selected input variables; and (iv) deriving the set-point equation by using the selected input variables; and (iv) deriving the set-point equation by combining the MRT regression equation and the operative temperature equation. This study aims to overcome the control
limitations of a conventional HVAC system by maintaining indoor comfort and enabling energy-efficientovercome the control limitations of a conventional HVAC system by maintaining indoor comfort and control in buildings.
The results of this study will contribute to maintaining comfortable indoorenabling energy-efficient control in buildings. The results of this study will contribute to maintaining environments during the summer months.
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More information: Eric Teitelbaum et al, Globe thermometer free convection error potentials, Scientific Reports (2020). DOI: 10.1038/s41598-020-59441-1
