1. Introduction
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,
19,
20,
21,
22,
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,
31,
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, which are difficult to measure and are used as an assumed value [
34,
35,
36,
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 dry-bulb 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; (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 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-efficient control in buildings. The results of this study will contribute to maintaining comfortable indoor environments during the summer months.
4. Conclusions
In this study, a method was suggested for deriving a cooling set-point temperature when considering the thermal characteristics of the subject building and the weather conditions, and its effect on improving the level of indoor comfort was tested. During this process, two main points should be considered: the MRT regression model was constructed and the daily optimal set-point temperatures were calculated using the predicted MRTs. The conclusions from this study are as follows:
To construct the MRT regression model, the following variables were used: (ⅰ) indoor temperature, (ⅱ) outdoor temperature, (ⅲ) sky type (cloud cover), and (ⅳ) time; further, a multiple regression analysis on the MRT was conducted. The results indicated an adjusted R² of 0.936, a CVRMSE of 2.57%, and an MBE of –0.03%, which satisfy the requirements in ASHRAE Guideline 14. As the results indicate, it was found in this study that the prediction of the MRT can be implemented using the above four variables.
The daily optimal set-point temperatures of the subject building calculated for the month of August ranged from 20.8 to 21.7 °C. This demonstrates that the set-point temperature used to maintain the indoor comfort level should vary in accordance with the changes in the daily weather conditions.
The comfort hour ratio of the new set-point temperature control was 93.6%, which improved the indoor comfort by 38.5% (26%p) from 67.6% when the MRT was not considered (23 °C set-point temperature control). Consequently, the new set-point temperature considering the thermal characteristics of the building and the weather conditions are regarded to be more effective at improving the indoor comfort than the existing set-point temperature.
The key point of the findings was that the set-point temperature should be modified by each building and the weather conditions, which was verified. In addition to such findings, there are still certain limitations, and future improvements are, therefore, required. Before implementing the method suggested in the present study, an indoor thermal environment dataset of the subject building should be established in advance through simulation modeling. In addition, the derived MRT regression model is limited to a certain building. Thus, the set-point temperature equation derived in this study shows a limitation in terms of the general applicability. In addition, because the thermal comfort is based on multiple thermal components, including the humidity, human activity, and clothing insulation, additional variables should be supported to accurately predict the MRT and should be considered to support the present research findings. Although this study applied the daily set-point temperatures in consideration of the equipment load, hourly intervals’, or other intervals’ set-point temperatures instead of daily set-point temperatures may be more efficient to create a comfortable indoor environment. Future studies will be conducted to derive a set-point temperature equation that can include various thermal-environment factors and the thermal characteristics of buildings based on more diverse data, and calculate hourly set-point temperatures, for which the present study can be applied as basic research. Furthermore, instead of the statistical regression analysis, future studies can consider the state-of-the-art technologies for prediction methods, such as machine learning, artificial intelligence, and ANN, which are widely used throughout the industry, especially by various building researchers [
34,
36,
50,
51,
52,
53]. Considering the trade-off between maintaining indoor comfort and reducing energy consumption, it might be difficult to view the control strategy suggested in this study as a fundamental solution to reduce the energy consumption. Therefore, an HVAC system control that can maintain a high level of indoor comfort and reduce energy consumption in a balanced manner should be developed in future studies.