Theoretical Study on Impact of Solar Radiation Heat Gain on Thermal Comfort and Energy Efficiency in Glass Curtain Wall Buildings Based on PMV Index

Abstract

With rapid global urbanization, glass curtain wall buildings have been widely adopted due to aesthetics and natural lighting. However, during summer time, intense solar radiation leads to significant indoor heat gain, which adversely affect thermal comfort and energy efficiency. The conventional air conditioning systems are typically equipped with a cooling capacity sufficient to maintain an indoor air temperature at the design values specified in the Design standard for energy efficiency of public buildings, which fails to account for the effects of radiation temperature, potentially resulting in reduced thermal comfort and energy inefficiency. By integrating the Thermal Comfort Tool to calculate the PMV index, this study evaluates the affection of solar heat gain on indoor occupants’ thermal comfort and proposes an optimized air temperature control strategy to realize thermal comfort. Based on the dynamic air temperature strategy, an energy consumption model is developed to evaluate the affection of solar radiation on energy consumption for glass curtain wall buildings based on the PMV index. The synergistic effects of shading measures are then evaluated. This study conducts simulation analysis using an office building with a glass curtain wall located in Beijing as a case study. When accounting for radiant heat gain, a significant portion of the time (53.89%) fall outside the thermal comfort range, even when the air conditioning is set to the designated temperature. To maintain thermal comfort, the air conditioning temperature must be lowered by 1.4–3.5 °C, resulting in a 28.08% increase in energy consumption. To address this issue, this study finds that installing interior shading can reduce radiant heat gain. Under the same thermal comfort conditions, the required air temperature reduction is only 0.8–2.1 °C, leading to a 24.26% reduction in energy consumption compared to the case without interior shading.

1. Introduction

With the rapid advancement of global urbanization, glass curtain wall buildings have become widely adopted in various architectural projects due to their modern aesthetics, excellent natural lighting, and spatial openness [1]. While these buildings provide a visually pleasing environment, they also experience significant solar heat gain during summer time [2]. This heat gain not only affects indoor temperatures, which increases the air conditioning load, but also poses challenges to occupants’ thermal comfort due to the radiation temperature [3]. The conventional air conditioning systems typically have a cooling capacity to regulate indoor environments by controlling the indoor air temperature at a fixed design value, which is normally 24–28 °C, referring to the air conditioning design code [4]. This method eliminates the air conditioning load from solar heat gain, but fails to account for the effects of solar heat gain, potentially leading to both reduced occupant comfort and energy efficiency [5].

Research indicates that human thermal comfort is closely related to various indoor environmental factors, such as temperature, humidity, and airflow velocity [6]. The Predicted Mean Vote model, an effective tool for assessing thermal comfort, provides valuable insights into how environmental conditions influence occupants’ thermal comfort perception [7]. Therefore, quantifying the impact of solar heat gain and optimizing the air conditioning temperature settings are crucial for enhancing both building energy efficiency and occupant comfort.

Kim et al. [8] conducted field experiments to collect objective, physical and subjective, personal data for thermal comfort analysis, revealing significant differences between the indoor air temperature and the mean radiant temperature (MRT) caused by the transparent envelope of buildings. Tse et al. [9] proposed a real-time PMV measurement system based on network transmission, which reduces the PMV calculation load through a lookup table method. The experimental results demonstrated its effectiveness in enhancing indoor thermal comfort and energy savings, achieving a 5.8% reduction in energy consumption under typical conditions. Hwang et al. [10] conducted field measurements to evaluate the impact of different types of glass curtain wall on indoor thermal comfort in subtropical climates. The study found that solar radiation induced significant increases in the MRT, which intensified thermal discomfort near the windows, particularly for those made of high-solar-heat-gain-coefficient (SHGC) glass. Li et al. [11] analyzed the effects of solar radiation on thermal comfort and physiological parameters in convection–radiation air conditioning environments through experiments. The results indicated that solar radiation significantly increased the participants’ local skin temperature. Hwang et al. [12] also simulated the performance of various glass curtain wall parameter combinations and proposed a visualization guide, balancing energy efficiency and thermal comfort. This method provides architects with optimal solutions under different parameter settings, enabling better design strategies to achieve equilibrium between energy savings and thermal comfort. Nie et al. [13] conducted a simulation study to analyze the relationship between the thermal insulation and indoor thermal comfort/energy consumption of rural buildings based on the adaptive thermal comfort (APMV) index. The results indicated that a wall heat transfer coefficient of 0.5 W/(m²·K) can maintain optimal indoor thermal comfort, while achieving significant energy savings. Sun et al. [14] used experimental methods to study the temperature distribution and energy-saving performance of Low-E double-glazed glass. Their results showed that Low-E glass, particularly double-silver Low-E glass, demonstrated an excellent energy-saving performance, effectively reducing temperature fluctuations within glass curtain wall structures and minimizing building energy consumption. Zhang et al. [15] developed a two-dimensional heat transfer model and conducted numerical simulations to study the impact of novel glass curtain walls filled with silica aerogel and phase-change materials on the indoor thermal environment. They found that the most indoor thermal comfort was achieved when the phase-change temperature was 26 °C. Li et al. [16] conducted field measurements of the building envelope surface temperature, as well as the indoor and outdoor air temperatures and humidity. Their findings revealed uneven temperature distribution, leading to regional differences in thermal comfort. Tian et al. [17] investigated the effect of the window-to-wall ratio on indoor thermal comfort through theoretical simulations and field experiments. Their study found that in summer, at 25 °C, the window-to-wall ratio was positively correlated with PPD, while at 29 °C, the relationship became inverse. Additionally, the thermal sensation index change rate in the operative temperature in summer was faster than the rate in winter.

Current research on thermal comfort primarily focuses on buildings with traditional wall structures, while limited attention has been given to glass curtain wall buildings, despite their widespread use in modern office environments. The conventional air conditioning systems typically rely on fixed cooling setpoints determined by design standards, requiring occupants to passively accept uniform indoor air conditions [18], without fully considering the direct impact of solar heat gain on human thermal comfort. In reality, solar radiation can act directly on the human body, significantly reducing perceived comfort. Compared to the conventional wall structures, glass curtain walls are more susceptible to fluctuations in surface temperatures, which not only influence the indoor air temperature, but also strongly affect occupants’ thermal comfort and the overall energy efficiency of the building.

Although previous studies have shown that solar radiation significantly increases people’s local skin temperature—particularly near window areas—there remains a lack of clarity regarding the extent to which the air conditioning setpoint temperature should be reduced to effectively restore thermal comfort under solar heat gain. Furthermore, the energy implications of such temperature adjustments have not been systematically evaluated. Existing research on solar heat gain in buildings with glass curtain walls has primarily relied on fixed air conditioning temperature settings, without considering energy consumption changes under equivalent thermal comfort conditions. Therefore, the quantitative impact of solar radiation on human thermal comfort, as well as its coupling with air conditioning control strategies, remains a critical issue requiring further investigation. Analyzing the energy consumption and conservation potential of different air conditioning control strategies under equivalent thermal comfort conditions holds substantial theoretical and practical significance.

This study evaluates the affection of solar heat gain on indoor occupants’ thermal comfort and explore air temperature control strategies based on the thermal comfort PMV index. Based on thermal comfort analysis, energy consumption affection by solar thermal radiation is simulated. Specifically, it compares fixed temperature settings with the PMV-based corrected temperature optimization strategy, evaluating their respective effectiveness and identifying optimal air conditioning temperature settings. The influence of shading measures on thermal comfort and energy efficiency was also analyzed. This paper seeks to provide scientific support for building design and air conditioning system optimization, contributing to the development of sustainable built environments.

2. Research Methods

As mentioned above, indoor thermal environment is a complex system influenced by multiple factors [19], and computational simulation techniques allow for the precise and acceptable analysis of how individual parameters affect both indoor thermal conditions and building energy consumption in this paper [20]. This study is conducted in multiple stages, encompassing model development, environmental parameter settings, and the evaluation of optimized air conditioning temperature strategies. To clearly illustrate the research process, Figure 1 presents a detailed workflow.

2.1. Simulation Tool

DeST-C was used for the simulation of building load and inner surface temperature, CBE was used to simulate the PMV value. Finally, we obtain the adjusted air-conditioning temperature.

DeST, developed by Tsinghua University, is annual dynamic energy consumption simulation software and a platform for simulating building thermal processes and energy performances [21]. The study of Zhu et al. compared three building energy simulation programs, Energy Plus, DeST, and DOE-2.1E, which indicated that when input parameters are aligned, the discrepancies between Energy Plus and DeST can be limited to within 10% [22]. The study in this paper utilized DeST to model building structures, calculate inner surface temperatures, and simulate energy consumption under various operating conditions.

The CBE Thermal Comfort Tool (2.5.6) is software developed by the Center for the Built Environment at the University of California, Berkeley, for calculating thermal comfort metrics (PMV and SET) in compliance with the ASHRAE 55, ISO 7730, and EN 16798-1 standards. In this study, the CBE Thermal Comfort Tool was used to calculate the indoor thermal comfort indicator PMV, and by fixing the comfortable PMV value, determine the appropriate air conditioning setpoint temperature.

2.2. Building Model

Field investigations of office buildings revealed that glass curtain wall designs are widely employed in modern office architecture [23]. While glass curtain walls enhance the architectural appearance of buildings, they also significantly increase solar heat gain, thereby affecting both human thermal comfort and building energy consumption [24]. To study the impact of solar heat gain on indoor thermal comfort, a typical glass curtain wall office building was selected as the simulation model. Figure 2 illustrates the floor plan of the building. The building consists of ten floors, each with a floor area of 1008 m². Figure 3 presents the southwest elevation of the building. The layout is simple and functional, meeting the typical requirements of office buildings.

2.3. Calculation Parameters

The simulation utilized he hourly meteorological data for Beijing, covering an entire year 8760 h. The specific meteorological parameter data is presented in Figure 4.

Based on the survey data and the Design standard for energy efficiency of public buildings [25], the window-to-wall ratio was set at 0.7. The building utilizes low-emissivity (Low-E) coated insulating glass with a solar heat gain coefficient of 0.426 and a thermal transmittance (U-value) of 2.1 W/(m²·K). Detailed parameters for the external walls, roof, and floors are provided in

Table 1. Enclosure parameter settings.

Envelope Structure	Thickness and Materials (From Outside to Inside)	Heat Transfer Coefficient (W/m2·K)	Thermal Resistance Values (m2·K/W)
External Wall	20 mm Cement Sand Mortar + 25 mm Cement Fiber Board + 100 mm Cellular Concrete + 240 mm Heavy Sand Clay Brick	0.90	0.95
Internal Wall	8 mm Gypsum Board + 60 mm Polystyrene Foam Plastic + 10 mm Gypsum Board + 240 mm Heavy Sand Clay Brick	1.5	0.43
Curtain Wall	Low-E Coated Double Glazing (Low Emissivity) [6 (Low-E) + 9 + 6]	2.1	0.48
Roof	20 mm Cement Sand Mortar + 200 mm Cellular Concrete + 130 mm Reinforced Concrete + 15 mm Cement Mortar	0.81	0.098
Floor Slab	25 mm Cement Mortar + 200 mm Reinforced 20 mm Cement Sand Mortar + 80 mm Reinforced Concrete + 20 mm Cement Sand Mortar	3.0	1.1

.

According to the Design code for heating ventilation and air conditioning of civil buildings [4], this study adopts 26 °C as the baseline temperature for air conditioning simulations. The cooling season is defined as the period from 1 June to 31 August, focusing on open-plan office spaces. In the simulation, the indoor air velocity was set to 0.1 m/s, and the relative humidity was maintained at 50%. The building adopts mechanical ventilation with an air exchange rate of 2 times per hour. During the operation of the air conditioning system, the windows remain closed. Curtains were installed and dynamically adjusted based on the experimental design; when solar shading was implemented, the curtains were fully closed; when shading was not applied, the curtains remained fully open. The other parameters are configured based on the relevant provisions of the Standard for lighting design of buildings [26]. All input parameters are summarized in

Table 2. Building model input parameters.

Parameters	Outdoor Air Temperature (°C)	Heat Transfer Coefficient (W/m2·K)	Indoor Air Temperature (°C)	Relative Humidity (%)
Inputs	Dynamically meteorological of DeST	Table 1	Summer: 26 °C	Summer: 50%
			Winter: 20 °C	Winter: 30%

.

The indoor thermal disturbance values are based on the default settings for office spaces in DeST-C. Detailed parameters are provided in

Table 3. Indoor thermal perturbation values.

Room	The Maximum Power of the Lights (W)	The Maximum Power of the Equipment (W)	Personnel Heat Load (W)	Personnel Wet Load (kg/hr)	Maximum Number of Occupants in the Room (Per)
Office	18	13	66	0.102	40
Toilet	5	0	61	0.109	8
Stairwell	5	0	58	0.184	8

. The schedules for occupant activity, lighting, equipment operation, and air conditioning are outlined in

Table 4. Schedule of personnel, fixtures, equipment and air conditioning operations.

Time Period	Personnel, Lighting, and Equipment Operating Hours (Working Days)	Personnel, Lighting, and Equipment Operating Hours (Non-Working Days)	Air Conditioning Runtime
0:00–8:00	OFF	OFF	OFF
8:00–12:00	ON (100%)	ON (30%)	ON (100%)
12:00–13:00	ON (30%)	ON (10%)	ON (100%)
13:00–17:00	ON (100%)	ON (30%)	ON (100%)
17:00–18:00	ON (50%)	ON (20%)	ON (100%)
18:00–20:00	ON (10%)	ON (10%)	ON (100%)
20:00–0:00	OFF	OFF	OFF

.

Based on the above parameter settings, the DeST (20230713) software was used to simulate the indoor environmental conditions, including air temperature, relative humidity, and interior surface radiant temperature. For the summer scenario, typical occupant activity levels were assumed, and reasonable values for indoor air velocity, clothing insulation, and metabolic rate were selected as input variables for PMV calculation. Radiant heat from solar gain and interior surface radiation also influence PMV values; the standard calculation methods for these factors are detailed in Section 2.4. Together, these parameters constitute the boundary conditions for both PMV evaluation and building energy consumption simulation.

2.4. Thermal Comfort Model

The traditional thermal comfort assessment methods normally rely on air temperature as the core indicator [27], which fails to comprehensively capture human thermal perception in glass curtain wall buildings, which gain a lot of heat.

The Predicted Mean Vote index, as a more comprehensive metric for evaluating indoor thermal comfort [28], is based on the principles of human thermal balance and integrates four environmental variables and two individual variables, enabling a more accurate representation of heat exchange between the human body and its surroundings, as well as overall thermal comfort. The PMV equation proposed by Fanger is given as follows [29]:

$$\begin{array}{l}PMV=[0.303\cdot e^{-0.036M}+0.0275]\times TL\\ =[0.303\cdot e^{-0.036M}+0.0275]\times \{M-W-3.05[5.73-0.007(M-W)-p_a]\\ -0.0173M(5.87-p_a)-0.0014M(34-t_a)-0.42(M-W-58)\\ -3.96\times {10}^{-8}{f}_{tl}[{(t_{cl}+273)}^4-{({\stackrel{\_}{t}}_r+273)}^4]-f_{cl}{\alpha }_c(t_{cl}-t_a)\}\end{array}$$

(1)

Compared to using air temperature alone as an evaluation criterion, the PMV model provides a more scientifically rigorous representation of dynamic changes in human thermal comfort [30]. As indicated by the above equation, one of the most critical parameters in PMV calculation is the mean radiant temperature (MRT) of the surrounding environment, especially in glass curtain wall buildings. MRT is influenced by two primary sources of radiation, (1) solar radiation and (2) heat radiation from the inner surface of the building envelope, as illustrated in Figure 5. To accurately determine MRT, the SolarCal model was employed for calculation [31].

The SolarCal model is based on the concept of the Effective Radiant Field (ERF) and is used to quantify the changes in net radiative flux on the human body [32]. When the surrounding surface temperature differs from the air temperature, the ERF represents additional long-wave radiation (positive or negative) acting on the human body. The surrounding surface temperature in a given space is typically represented by the MRT [33]. The relationship between the ERF and the MRT, which accounts for long-wave radiation exchange with surfaces, is given as follows:

$$ERF=f_{eff}{h}_r(MRT-T_a)$$

(2)

In Equation (2), f_eff represents the fraction of the body surface area exposed to environmental radiation, varying with posture: 0.696 for a seated individual and 0.725 for a standing individual [34]. Additionally, the absorption of solar radiation by surfaces can be approximated as an additional long-wave flux, ERF_solar. This effect is quantified in Equation (3) using E_solar.

$${\alpha }_{LW}ER{F}_{solar}={\alpha }_{SW}{E}_{solar}$$

(3)

In Equation (3), E_solar represents the shortwave solar radiation flux (W/m²) incident on the human body. α_SW is the shortwave absorptivity, while αLW is long-wave absorptivity. E_solar is composed of three components, each accounting for window-related factors and distributed across the body surface: (1) direct solar radiation from the sun (E_dir), (2) diffuse radiation from the sky dome (E_diff), and (3) solar radiation reflected from the ground (E_refl). These components are defined in Equations (4)–(6) as follows [33]:

$$E_{diff}=0.5f_{eff}{f}_{svv}{T}_{sol}{I}_{diff}$$

(4)

In Equation (4), f_svv represents the visible fraction of the sky dome in the field of view, calculated according to Equation (9). I_diff denotes the diffuse irradiance incident on the upward-facing surfaces. T_sol is total solar transmittance.

$$E_{refl}=0.5f_{eff}{f}_{svv}{T}_{sol}{I}_{TH}{R}_{floor}$$

(5)

In Equation (5), ITH represents the total horizontal outdoor irradiance, including both direct and diffuse components, measured in W/m². R_floor is the reflection coefficient of the floor, accounting for both shortwave and long-wave radiation contributions, with a typical value of approximately 0.5 (shortwave 0.2 + long-wave 0.3) [33].

Direct radiation only affects the projected area of the human body (A_p) and is influenced by obstructions within the indoor environment [33]. Its expression is as follows:

$$E_{dir}=({\displaystyle \frac{A_p}{A_D}})f_{bes}{T}_{sol}{I}_{dir}$$

(6)

In Equation (6), Ap represents the projected area of the standard human body under direct sunlight. A_D is the assumed DuBois body surface area (approximately 1.8 m²). f_bes is the proportion of the body exposed to sunlight. Idir denotes direct solar irradiance (normal incidence) in W/m². The relationship between the meteorological radiation parameters is as follows [33]:

$$I_{TH}=I_{dir}\sin \beta +I_{diff}$$

(7)

In Equation (7), β represents the solar altitude angle.

Therefore, ERF_solar can be calculated using the following Formula (8) [35]:

$$ER{F}_{solar}=[0.5f_{eff}{f}_{svv}(I_{diff}+I_{TH}{R}_{floor})+{\displaystyle \frac{A_p}{A_D}}{f}_{bes}{I}_{dir}]\cdot T_{sol}\cdot {\displaystyle \frac{{\alpha }_{sw}}{{\alpha }_{Lw}}}$$

(8)

To simplify the meteorological data input for the SolarCal model, I_diff can be estimated using an empirical relationship $I_{diff}=0.17I_{dir}\sin \beta$. Finally, ERF_solar was added to the long-wave radiation contribution of the ERF, allowing or the calculation of the corrected solar-adjusted MRT (ΔMRT) using Equation (8). This correction enabled the calculation of PMV that accounts for the effects of shortwave radiation, providing a more accurate assessment of human thermal comfort.

The CBE Thermal Comfort Tool integrates major thermal comfort models, including the PMV model, the Standard Effective Temperature (SET) model, and the adaptive model [36]. Additionally, it can utilize the aforementioned SolarCal model to compute the ERF value affected by solar radiation heat gain and the corrected MRT, providing intuitive thermal comfort evaluation results for various building environment variable combinations.

To ensure the simulation aligns with real scenarios, the activity level of occupants was assumed to be typical office work such as typing, with a clothing insulation value set to 0.5 clo, representing typical summer attire. The relative humidity was set to be 50%, while the other specific parameters are detailed in

Table 5. CBE software initial setting.

Parameters	Air Temperature (°C)	Mean Radiant Temperature (°C)	Air Speed (m/s)	Relative Humidity (%)	Metabolic Rate (met)	Clothing Level (clo)
Inputs	26 °C	Temperature as affected by solar heat gain	0.1	50	1.1	0.5

.

Calculating the mean radiant temperature using the CBE (2.5.6) software requires inputting several key parameters, including the inner surface temperatures of the building envelope, the solar altitude angle, and solar radiation intensity. The air temperature was fixed at the initial setpoint of 26 °C, while the inner surface temperatures were determined using DeST software. Similarly, the solar altitude angle and total solar radiation were derived from the meteorological data for Beijing provided by DeST.

The solar horizontal angle relative to the front of a person (SHARP) was calculated based on azimuth and angular relationships. Total solar transmittance (T_sol) represents the window’s transmittance performance. This study utilized low-transmittance Low-e coated double-glazed windows with a solar heat gain coefficient (SHGC) of 0.33. When shading curtains were applied, the effective transmittance was further reduced by the curtain’s transmittance of 0.5, resulting in a final transmittance of 0.165.

The sky vault view fraction (f_svv) depends on the window’s height, width, and the distance between the measurement point and the window. It was calculated using the following Formula (9):

$$f_{svv}\approx {\displaystyle \frac{{\tan }^{-1}(\frac{h}{2d})\cdot {\tan }^{-1}(\frac{w}{2d})}{90°\times 180°}}$$

(9)

In this equation, h represents the window height (m). w represents the window width (m). d denotes the distance between the measurement point and the window (m). Substituting the input data, the calculated f_svv was 0.067.

The fraction of body exposed to the sun (f_bes) was determined using solar shadow simulation analysis. During the period from 15:00 to 16:00, 30% of the body was exposed to sunlight at the measurement point, resulting in an f_bes value of 0.3. If shading curtains were installed, this value was set to 0. Additionally, average shortwave absorptivity (α) depends on skin and clothing color and material. An average value of 0.67 was selected as a reasonable estimate for this study [37].

After inputting the nine parameters into the CBE software, the ERF value was calculated. Using the relationship between the ERF and the MRT, the MRT was then determined. This result provides a crucial data foundation for calculating the PMV, establishing a scientific basis for subsequent thermal comfort analysis.

2.5. Working Condition Setting

To investigate the impact of solar heat gain on human thermal comfort, this study examines two glass curtain wall building conditions through simulation analysis. The methodology is outlined as follows:

(1)

Simulation without Shading

First, the mean radiant temperature was simulated using DeST and SolarCal. Parameters such as mean radiant temperature, air temperature, airflow velocity, relative humidity, and clothing thermal resistance were input into the CBE Thermal Comfort Tool to calculate the temperature change and thermal comfort index.

Subsequently, the air conditioning setpoint was adjusted to bring the PMV within the comfortable range for the human body. The corrected air conditioning set point was fed back into DeST for load simulation and calculation. Finally, the changes in the air conditioning setpoint under solar heat gain conditions were analyzed, along with the corresponding increase in energy consumption, and the linear relationship between the air conditioning setpoint and total solar radiation was derived.

(2)

Simulation with Shading

With the shading curtain on the glass curtain wall fully closed (100% deployment), the room’s cooling load was re-simulated. The same methodology was used to adjust the air conditioning setpoint temperature to achieve a comfortable PMV value. DeST was then employed to calculate the energy consumption of the air conditioning system using the shading strategy. The results were compared with those from the simulation without shading. This analysis quantified the effectiveness of shading measures in reducing radiant heat gain, lowering air conditioning energy consumption, and determined the specific energy consumption differences, while maintaining thermal comfort.

3. Results and Discussion

3.1. Influence of Solar Heat Gain on MRT Based on Thermal Comfort

In glass curtain wall buildings, occupants’ thermal sensation is influenced by both heat transfer through windows and walls and direct solar radiation. These factors alter the mean radiant temperature, thereby affecting indoor thermal comfort. In this study, the DeST software was used to simulate the hourly inner surface temperatures of office building walls during the air conditioning season. The results are illustrated in Figure 6.

The hourly inner surface temperature data from the simulation, along with the meteorological parameters for Beijing, were imported into the SolarCal model to calculate the mean radiant temperature.

This study analyzes the variation in the indoor MRT under different levels of solar radiation intensity based on the simulation data. To facilitate analysis, the solar radiation intensity was grouped into intervals of 100 W/m², encompassing the full range of solar radiation during the cooling season. The average air conditioning setpoint temperature within each interval was calculated to simplify data processing, while maintaining scientific rigor and comparability. Figure 7 illustrates the impact of solar radiation intensity on the MRT under both the shaded and unshaded conditions.

Figure 7 illustrates that as the solar radiation intensity increases, the mean radiant temperature rises under both the shaded and unshaded conditions, with a significantly steeper increase observed in the unshaded scenario. Specifically, under the unshaded conditions, the MRT increases from 27.7 °C to 30.15 °C, an increment of 2.45 °C. This rise in MRT significantly reduces thermal comfort. In contrast, shading measures effectively mitigate the MRT increase, limiting the overall rise to 1.48 °C, demonstrating the substantial effectiveness of shading.

The MRT difference between the shaded and unshaded conditions is minimal at low radiation intensities (approximately 0.7 °C). However, this difference widens with increasing radiation, peaking at around 1.75 °C within the 500–600 W/m² radiation range. According to the PMV model, an increase in the MRT substantially raises the cooling load demands. Thus, shading measures can effectively lower the MRT, reducing the cooling load demands, while enhancing thermal comfort.

In conclusion, solar radiation significantly elevates the MRT, thereby increasing the cooling load demands. Implementing shading measures effectively reduces the MRT, achieving both lower cooling loads and improved thermal comfort. The findings of this study are consistent with those of Hwang et al. [10], who reported that solar radiation significantly increases the MRT, particularly in areas with high-SHGC glass, thereby exacerbating thermal discomfort near windows. This study further quantifies the variation in MRT under different levels of solar radiation and extends the analysis to explore dynamic indoor air conditioning temperature settings and their impact on energy consumption under equivalent thermal comfort conditions.

3.2. Influence of Solar Heat Gain on PMV Values Based on Thermal Comfort

The simulated MRT, along with indoor air temperature, humidity, and the other parameters set in DeST, were imported into the CBE Thermal Comfort Tool to calculate the hourly PMV values for the room. This study focuses on analyzing the impact of solar radiation heat gain on thermal comfort. Therefore, the air conditioning season (from 1 June to 31 August) was selected as the study period to represent typical summer conditions.

Figure 8 illustrate the PMV data distributions under the two different conditions, as calculated using the CBE tool. To analyze the distribution characteristics of the PMV more intuitively, the data were grouped based on the PMV fluctuation intervals (at 0.25 intervals). The histogram is used to represent the number of hours within each interval, while the line graph depicts the cumulative proportion of hourly occurrences.

The analysis shown in Figure 8 demonstrates that solar heat gain significantly impacts indoor thermal comfort, particularly in the absence of curtains, where the number and proportion of hours with PMV values outside the comfort range are notably higher.

Specifically, without the use of curtains, the PMV values were primarily concentrated within the range from 0.25 to 0.75, indicating a tendency toward a warm indoor thermal environment. Notably, the number of hours with PMV values exceeding 0.5 reached 595, accounting for 53.89% of the total hours, which significantly surpasses the upper limit of the thermal comfort range.

In contrast, after the implementation of curtains, most PMV values fell within the range from 0.25 to 0.5, with 712 h, representing 64.49% of the total. Furthermore, the number of hours with PMV values within the comfort range [−0.5,0.5] increased from 509 (without shading) to 984, marking an improvement of nearly 50%.

The cumulative percentage curves further illustrate the efficiency of shading measures in improving the indoor thermal environment. With curtains installed, the cumulative percentage curve (pink) reached 90% at a PMV value of 0.5, indicating that the PMV values remained within the comfort range for most of the time. In contrast, the curve for the unshaded condition (blue) remained below 50% at the same point, revealing a clear disparity. This highlights the effectiveness of curtains in mitigating heat-related discomfort.

In conclusion, solar heat gain significantly affects human thermal comfort. A uniform air conditioning temperature setting of 26 °C, as air condition design code, is insufficient to maintain comfort for over half of the time. Therefore, adjusting the air conditioning temperature dynamically based on changing conditions is essential to keep the PMV within the comfort range. These findings reaffirm the necessity of dynamic temperature optimization strategies.

3.3. Influence of Solar Heat Gain on Air Conditioning Set Temperature Based on Thermal Comfort

In this study, key parameters, such as the mean radiant temperature and solar radiation intensity, were input into the CBE Thermal Comfort Tool for computational analysis. According to the software results, when the indoor air conditioning temperature is set to 26 °C, according to air condition design code, the PMV value is 0.11, indicating a good thermal comfort state. Therefore, this study adopts a PMV value of 0.11 as the target and adjusts the air conditioning set temperature to maintain indoor comfort under varying solar radiation conditions.

To facilitate analysis and data processing, solar radiation intensity was grouped into intervals of 100 W/m². A curve illustrating the variation in the air conditioning set temperature with solar radiation intensity was plotted, as shown in Figure 9. The curve visually demonstrates the trend of air conditioning set temperature adjustments in response to increasing solar radiation, reflecting the dynamic relationship between indoor thermal regulation and solar heat gains. This curve provides a crucial reference for optimizing air conditioning temperature settings and serves as a basis for developing energy-efficient operation strategies in actual buildings.

Figure 9 shows that as solar radiation increases, the air conditioning set temperature decreases, indicating that maintaining the same thermal comfort parameters requires dynamic adjustments based on the current weather conditions, rather than a fixed temperature setting. Under the no-shading condition, the air conditioning set temperature decreases from 24.6 °C to 22.5 °C, with the set temperature dropping by approximately 0.19 °C for every 100 W/m² increase in solar radiation. In the lower radiation range from (0,100] to (300,400], the temperature decrease is relatively rapid, suggesting that initial increments in solar radiation have a significant impact on indoor thermal gains. However, in the higher radiation range (600,1100], the temperature decrease becomes more gradual.

Under the shading condition, the air conditioning set temperature decreases more gradually with increasing solar radiation from 25.2 °C to 23.9 °C. The temperature change is much smaller than under the no-shading condition, with the set temperature dropping by only about 0.13 °C for every 100 W/m² increase in solar radiation. Compared to the no-shading condition, the temperature decrease in the lower radiation range from (0,100] to (300,400] is slower, confirming that shading measures effectively reduce solar heat gain. However, in the higher radiation range (800,1100], the temperature decrease slightly increases, which could be due to the shading measures being unable to fully block high-intensity solar radiation, though they still significantly mitigate its heat gain effect. The findings of this study are consistent with those of Pan et al. [3] and further expand upon their conclusions. Their research demonstrated that indoor solar radiation significantly influences the thermal sensation by increasing people’s local skin temperature and triggering physiological responses. This paper complements these findings by highlighting that maintaining equivalent thermal comfort under increasing solar radiation requires dynamic adjustments to the air conditioning setpoint temperature. Furthermore, we quantified the necessary temperature adjustments corresponding to varying levels of solar radiation, thereby providing a more detailed basis for thermal comfort control strategies.

In conclusion, an increase in solar radiation leads to a decrease in the air conditioning set temperature to keep maintain the thermal comfort level, demonstrating that solar heat gain is a primary factor in regulating air conditioning settings. This confirms that the addition of shading measures effectively reduces the cooling demand for air conditioning, achieving a balance of higher energy efficiency and comfort when both these factors are combined.

3.4. Influence of Solar Heat Gain on Air Conditioning Energy Consumption Based on Thermal Comfort

Building energy consumption is significantly influenced by solar radiation, with solar heat gain notably impacting air conditioning energy use [38]. This effect is particularly pronounced in glass curtain wall buildings, where solar radiation directly penetrates the interior, increasing the cooling load demand. To investigate the impact of solar heat gain on air conditioning energy consumption, this study conducts simulation analyses under different temperature control strategies, including a fixed temperature setting 26 °C and an adjusted temperature setting, as well as combinations of conditions with and without curtain shading. Figure 10 illustrates the seasonal cumulative cooling load of the air conditioning system under these varying conditions.

Figure 10 clearly demonstrates that under conditions without curtains, the cumulative cooling load for air conditioning with temperature adjustments based on thermal comfort is significantly higher than the cooling load when the temperature is constantly set at 26 °C. The cumulative cooling load increases by approximately 28.08%. This indicates that in order to meet the thermal comfort requirements, the air conditioning system requires higher energy consumption.

In contrast, with curtain shading, the cumulative cooling load of the air conditioning system is notably reduced.

(1)

Under constant temperature settings, the cumulative cooling load decreases to 31,526.84 kWh, representing a reduction of approximately 16.89% compared to that of the no-curtain condition.

(2)

With corrected temperature adjustment, the cumulative cooling load further decreases, achieving a reduction of approximately 24.26% compared to that of the no-curtain condition.

The results indicate that curtains, as a shading measure, effectively reduce solar radiation penetration, significantly mitigating the impact of solar heat gain on air conditioning systems and lowering energy consumption, while also contributing to thermal comfort.

This comprehensive analysis reveals that solar heat gain is a key factor influencing air conditioning energy consumption. The strategic use of shading devices such as curtains plays a crucial role in balancing thermal comfort and energy efficiency.

4. Conclusions

This study investigates the impact of solar heat gain on optimizing air conditioning temperature settings in glass curtain wall buildings, focusing on ensuring thermal comfort, while improving energy efficiency. Through dynamic simulations and analyses, the following key conclusions are drawn:

(1)

Dynamic temperature adjustment is critical for thermal comfort

Solar heat gain significantly affects thermal comfort. When the air conditioning temperature is uniformly set at 26 °C according to the air condition design code, a comfortable in-door environment cannot be maintained over 50% of the time during the three-month cooling season. Dynamically adjusting the air conditioning settings based on the changing conditions is essential for keeping the PMV within the comfort range. This finding underscores the importance of dynamic optimization strategies.

(2)

Solar heat gain necessitates lower temperature settings

Regardless of whether shading measures are implemented, increased solar radiation leads to a decrease in the air conditioning temperature, emphasizing the critical role of solar heat gain in temperature regulation. A negative correlation between the solar radiation levels and the temperature settings was observed in both the shaded and non-shaded scenarios. In the lower radiation range ([0–400] W/m²), the air conditioning temperature decreases more rapidly. Shading measures effectively reduce the cooling demand, demonstrating that the combination of shading and dynamic temperature adjustment achieves a better balance between energy efficiency and thermal comfort.

(3)

Solar heat gain is a major factor influencing air conditioning energy consumption

Solar heat gain significantly increases air conditioning energy consumption, with an estimated rise of approximately 28% when its effects are considered. Implementing shading measures such as curtains can alleviate this impact, reducing energy consumption by around 20% compared to that of non-shaded conditions. This approach balances thermal comfort and energy savings, highlighting the potential of shading strategies in optimizing air conditioning control.

These findings confirm the substantial impact of solar heat gain on thermal comfort and provide valuable insights for optimizing air conditioning temperature settings to address solar heat gain.