Effect of Ventilation Strategies of Center-Mounted Louver Ventilation Window on Building Energy Consumption and Daylighting

Abstract

An innovative center-mounted louver ventilation window suitable for seasonal ventilation strategies was proposed, combining the regional climate of Qingdao. The sustainable development concept is embodied, which can not only reduce building energy consumption but also consider the quality of daylighting. This research constructed a comprehensive evaluation framework, taking an office building in Qingdao as an example. The framework utilized the parametric design platform Grasshopper and its environmental design plug-in Ladybugtools. It considered the daylighting performance and energy consumption of the building. This study included six different ventilation strategies, with energy use intensity and useful daylight illuminance as evaluation indicators. The results indicated that the seasonal ventilation strategies and parameters of blinds were optimized to significantly improve the energy efficiency of the building without compromising daylighting quality. The optimized solution reduces energy consumption from 83.81 kWh/m² to 55.0 kWh/m², achieving a 34.4% reduction while maintaining a high UDI. This energy-saving effect reveals the influence of different ventilation strategies on energy and daylighting. And it provides an important reference for sustainable design in similar climate contexts.

1. Introduction

Energy shortage and environmental pollution have become the main challenges restricting human development, especially in the context of rapid global economic growth. The construction sector, a significant energy consumer, contributes approximately 30% of the world’s total energy consumption during its operational phase, a figure that is on an upward trend. This issue is especially pronounced in developing countries experiencing rapid growth. For instance, China’s 2020 Building Energy Research Report indicates that the annual energy consumption in the construction sector’s operational phase has reached 1 billion tons of standard coal, representing 21.7% of the nation’s total energy consumption [1]. Confronted with the problem of climate change, the Chinese government has set a “dual carbon” strategic objective: to peak carbon emissions by 2030 and achieve carbon neutrality by 2060. To realize these ambitions, the construction sector must achieve breakthroughs in energy conservation. Consequently, the acceleration of clean energy technology innovation, the intensification of research into energy-saving strategies during the building operational phase, and the optimization of building energy systems have become the foremost tasks in the current development of building energy conservation.

Architectural design methods are undergoing a shift from experience-oriented to precise quantitative analysis to meet the challenges of building energy conservation. The application of many computer technologies has replaced traditional manual operation methods. The combination of building performance simulation and parametric design plays an important role in modern architectural design. This reflects the ongoing trend in the field [2,3,4]. Parametric design gives architects the flexibility to quickly adjust building structures with adjustable parameters without the need to redraw drawings. This study adopts a parametric performance design method and establishes a one-click simulation process by using the Grasshopper platform and Ladybugtools. At the same time, it can be used for performance simulation in any region of the world. It only needs to input the building model and its latitude and longitude coordinates to run. In order to ensure its accessibility in areas with limited technical resources, Grasshopper has a wide user community and rich online learning resources. Even in resource-constrained environments, designers and researchers can quickly get started, with a low learning threshold. This provides more variables for performance simulations, enabling detailed analysis of aspects such as energy efficiency and daylighting at an early stage of design [5]. At the same time, it also provides a data basis for future trade-offs between goals. Embedding performance simulations into the parametric design process not only enhances the science of the design but also enables architects to optimize goals more comprehensively to meet contemporary society’s demands for sustainability. In addition, from an economic perspective, considering the building’s life cycle, the cost of performing a small transformation in the building design stage or in the early stage of the building performance simulation is much lower than that of late demolition and reconstruction. This is compared to the cost of performance simulation optimization during the design stage.

The practice has proved that the integration of performance evaluation and energy-saving strategies in the design stage can effectively avoid the high cost of later transformation and has economic benefits [6]. Building performance simulation and optimization in the early stage has become a key means to achieve energy saving, especially in the context of global climate change [7]. By establishing an assessment system, precise passive energy-saving strategies can be developed according to the climate characteristics of different regions, thus ensuring the energy efficiency of buildings [8]. This not only ensures that the building achieves the desired energy efficiency but also balances the return on investment with environmental benefits.

Openings in buildings, such as windows and doors, are key elements of the building facade and the interface between the interior and the external environment [9]. The window system plays an role in the building’s daylighting and heat penetration and is the most important part of the building’s thermal performance [10]. Ali Kangazian et al. were able to improve the visual comfort of occupants and the energy efficiency of buildings by combining glass and shading to form a daylight control system (DCS) [11]. Therefore, the design of the window system is crucial and needs to consider its high thermal conductivity [12]. In recent years, research on the building envelope has focused on optimizing the window system [13], while the traditional evaluation system has focused on daylighting performance, energy consumption, and thermal comfort [14]. To reduce building energy consumption, in addition to applying conventional shading strategies [15], integrating and optimizing passive design strategies is also very important [16]. The changes in outdoor temperature are related to the operation of the windows [17]. Some government research and scientific research have found that certain diseases are more easily transmitted in open spaces such as offices, restaurants, and schools, especially after the global pandemic [18]. Therefore, opening windows for ventilation is a solution to improve indoor air quality [19]. For example, the governments of countries such as the UK encouraged people to regularly open windows during the pandemic [20,21]. People’s physiological regulation of heat shock in natural ventilation environments is stronger than in air-conditioned environments [22]. In addition to benefiting human health, natural ventilation buildings have lower energy costs. These costs are about 40% lower than those of air-conditioned buildings, and this helps the energy efficiency strategies needed to meet sustainable energy goals [23,24]. Although there have been many studies on the ventilation of the building envelope by scholars at home and abroad [25,26,27,28], most of the studies have been based on simulations using the ANSYS Fluent 19.2 software. Simulation studies based on the Grasshopper parameter design platform are relatively less. Therefore, this study aims to optimize the window system in the building envelope using the Grasshopper platform. Although less is known about the natural ventilation of ventilation shutters, some similar experiments and simulation studies have been carried out by scholars in the past [29]. Movassag et al. studied the effect of louvers on indoor solar heat gain control and natural ventilation in office buildings [30]. By examining the ventilation performance of rooms with different louver angles, they came to the conclusion that the louver angle has a potential influence on ventilation [31]. Based on the traditional louver window, this study integrates adjustable ventilation strategies to meet the needs of different seasons to improve the system’s energy efficiency [32,33,34]. The window system in this study fully utilizes natural ventilation to reduce air-conditioning energy consumption. In summer, the hot pressure effect is used to drive ventilation through the cavity to accelerate heat loss. In winter, the greenhouse effect of the cavity is used to preheat and improve the heating efficiency. Based on this design concept, the system enables year-round ventilation optimization, opening new technical ways to improve the performance of the building envelope. At the same time, it also provides a new research perspective and practical application value for the development of green buildings and energy-saving buildings.

Regarding the research on window energy consumption and daylighting, the most relevant literature divides the research methods into two categories: experimental research and software simulation. Through the long-term monitoring and data collection of existing buildings, natural ventilation characteristics and daylighting performance are analyzed. The software simulation involves building an idealized model. This model evaluates the performance of the system under different meteorological conditions. It provides theoretical support for optimizing ventilation strategies.

With the development of computer-aided design technology, the optimization methods of building performance show a diversified trend [35]. Although the single-objective optimization method is simple to operate, it is often difficult to meet the requirements of multi-dimensional indicators in architectural design. In contrast, multi-objective optimization can consider multiple performance objectives simultaneously. However, its convergence and computational efficiency may be limited. The complexity of the objective function and the dimension of the parameter space limit it. In this case, the exhaustive method provides a way for steady optimization. By systematically traversing all the possible combinations of design variables, the exhaustive method not only ensures that the global optimal solution is found but also fully reveals the mapping relationship between design parameters and performance indicators. It is advantageous during the early stages of a building project, where design variables and their value ranges are relatively limited. At this stage, the computational burden of the exhaustive method is manageable, yet it provides a comprehensive foundation for informed decision-making. With advancements in computing technology, designers can efficiently generate a complete performance database, establishing the foundation for the scheme optimization of design variables. Additionally, the extensive data set produced by the exhaustive method can be used as training samples for developing data-driven rapid prediction models in subsequent stages.

In this study, because the research object is a single building and the number of parameters involved is small, this enables the generation of simulation results for all parameter combinations in short computational times, allowing a comprehensive assessment of the impact of different parameters on performance. However, it should be noted that the computational requirements of the exhaustive method increase exponentially with the number of parameters, so the computational cost may increase significantly for cases with more parameters or larger project sizes. In contrast, other optimization methods such as genetic algorithms or particle swarm optimization show higher computational efficiency when dealing with high-dimensional problems or complex projects because they can reduce the full exploration of the parameter space through intelligent search strategies. Therefore, choosing different optimization methods needs to weigh their advantages and disadvantages based on the project size and parameter complexity of computing resources. In this paper, the exhaustive method is used to optimize the center-mounted louver ventilation windows in office buildings. Its strategies improve the building’s operation across different seasons and meteorological conditions. This optimization enhances daylighting quality and reduces energy consumption and contributes to the building’s sustainability. Figure 1 illustrates the framework of this article.

Based on existing relevant studies, this study strives to achieve efficient energy-saving effects by optimizing some parameters. The goal of this research is to achieve more significant results in building energy efficiency and to provide more innovative solutions in the field of sustainable building design.

2. Methods

2.1. Case Study Description

The case room is located on the sixth floor of Qingdao University of Technology. It is a multi-person office that can accommodate up to ten people. This is a typical office building in Qingdao. At the same time, this is also a renovation project that our research team is working on, so the author can directly feel the shortcomings in shading, ventilation, and energy consumption, so as to carry out the targeted research. The office has the typical layout characteristics of a multi-person office space, and its architectural design and use mode are representative of Qingdao and other areas with similar climatic conditions. Therefore, choosing this space as a case study not only helps to verify the effectiveness of the research method but also provides reference value for office buildings in similar areas and proposes practical solutions for optimizing the office environment. The total area of the room is 71 m² with a width of 8 m and a depth of 9 m. The northwest side of the room is recessed inward by 1 m². The unique geometry of the room is real and is caused by the need to leave enough space for the entrance of the adjacent room. To ensure the accuracy of the simulation results, this study modeled the model consistent with the actual environment to more realistically reflect its performance. But the room in this study can be completely regarded as a rectangle, and the recessed wall will not affect the accuracy of the simulation results. The analysis methods and performance evaluation indicators used in this study are widely applicable and can be flexibly adjusted for different building designs. The room faces south and has three windows, each measuring 2.61 m² (1.45 m × 1.8 m). The window-to-wall ratio of the south wall is 0.29, which not only ensures sufficient daylighting in the room but also controls excessive heat loss. These characteristics provide a good basis for the thermal environment and ventilation performance in the laboratory. Figure 2 illustrates the case room and experimental equipment. Figure 2a shows the room layout, while Figure 2d confirms that the outer wall is unobstructed by nearby structures. A meteorological station is installed on the building’s roof (Figure 2b) to collect data on dry bulb temperature, relative humidity, and solar radiation at set intervals. These data will be used for model validation in subsequent chapters. The room entrance is located on the north side, with adjacent rooms to the east and west, as shown in the red box of Figure 2d. There are no rooms above the office, and the windows are unobstructed, creating an ideal research environment. Figure 2c,e display illuminance meters, thermometers, humidity meters, and the outdoor meteorological station, enabling comprehensive monitoring of both indoor and outdoor conditions.

2.2. Climate Condition in Qingdao

Qingdao is located in the southern part of China’s Shandong Peninsula, between 119°30′ and 121°0′ east longitude and 35°35′ and 37°09′ north latitude. The city belongs to the northern temperate monsoon climate zone, and according to China’s building thermal zoning, Qingdao is classified as a cold area. Qingdao has a unique geographical location, surrounded by the sea on three sides, influenced by the southeast ocean monsoon and ocean current. So, it has obvious maritime climate characteristics [36]. The climate of Qingdao is characterized by four distinct seasons, abundant annual precipitation, humid air, and moderate temperature. According to (the meteorological data come from Climate.onebuilding.org, accessed on 25 September 2024), average temperatures in Qingdao range from −4.5 °C to 28.5 °C all year round. January is the coldest month of the year, with an average temperature of −2.06 °C at 6:00 AM and 2.10 °C at 3:00 PM. Conversely, August is the hottest, with an average temperature of 23.13 °C at 6:00 AM and 29.12 °C at 3:00 PM.

The climate of Qingdao is mild due to the influence of the ocean, especially in spring, when the ocean plays a significant role in regulating the temperature. Qingdao is classified as a “cold region” in China’s climate zoning, which primarily emphasizes heating needs in the winter. However, the region’s climate conditions are also characterized by hot and humid summers, so overheating issues must be considered. In the summer, the design must consider minimizing solar heat gain to reduce cooling loads. Strategies such as using adjustable shading devices and utilizing natural ventilation are critical. In the design and performance optimization, it is necessary to consider various factors to achieve a balance of daylighting and energy efficiency in the buildings.

As far as solar radiation is concerned, Qingdao is relatively rich in solar radiation resources throughout the year as shown in Figure 3. In terms of season, spring is the highest, followed by summer and autumn, and winter is the lowest. Figure 4a, Figure 4b, and Figure 4c, respectively, show the direct, indirect, and total sky radiation maps of Qingdao for the whole year, revealing the spatial distribution characteristics of accumulated radiation throughout the year. In architectural design, reasonable control of solar radiation entering the room is important for shading design. In Figure 4d, the blue part represents a negative value, which means that the harmful radiation exceeds the beneficial radiation in the annual sky radiation in this direction. The red part is positive, meaning that the beneficial radiation exceeds the harmful radiation. The darker the color, the greater the absolute value. It can be seen that the harmful radiation mainly comes from the sky area with a large altitude angle of the sun, especially the west side of the sky. The beneficial radiation mainly comes from the direction of the sky where the sun’s altitude angle is lower, and the eastern side is slightly more. The thresholds for beneficial and harmful radiation are determined below. First, a comfortable temperature range needs to be determined. According to the study of the thermal comfort temperature of office buildings in Dalian, a cold region also close to the ocean, by the Chinese research team Sun et al., 172 people were selected by random sampling for on-site testing, and questionnaires were filled out after completion [37]. According to the experimental results, the temperature range of 16~23.5 °C, which is acceptable to 80% of the personnel, is taken as the comfortable temperature range. Then, the radiation in the sky when the outdoor temperature is lower than the lower limit of the range (16 °C) is set as beneficial radiation, because the solar radiation at this time is conducive to heating the indoor air and increasing the perceived temperature of people. When the outdoor temperature is higher than the upper limit of the range (23.5 °C), the solar radiation in space is defined as harmful radiation, because the solar radiation at this time will increase the body’s sense of heat and aggravate the discomfort. This is the threshold for harmful and beneficial radiation.

2.3. Baseline Model Settings

In the simulation research, the baseline model was constructed through the Grasshopper parametric design platform and plug-in Ladybugtools. At the same time, to simulate the environment realistically, parametric models of adjacent rooms and surrounding elements were constructed. As shown in Figure 5, the test room is only in direct contact with the outside in the south direction, while the east, west, and north directions are set as heat insulation, which is conducive to operational simulation, where the blue solids are glass walls and the dashed lines are windows.

The parameters for the building in the model have been adjusted to reflect the actual situation.

Table 1. Opaque material properties for daylight simulation.

Building Elements	R Reflectance	G Reflectance	B Reflectance	Roughness	Specularity
White wall	0.9	0.9	0.9	0.65	0.0064
Dark gray ceiling	0.17	0.17	0.17	0.002	0.005
Light gray floor	0.42	0.42	0.42	0.001	0.02

and

Table 2. Transparent material properties for daylighting simulation.

Glass Elements	R Transmittance	G Transmittance	B Transmittance	Refraction
Interior	0.65	0.65	0.65	1.52
Exterior	0.58	0.58	0.58	1.52

present the settings for the building’s optical parameters, while

Table 3. Opaque material properties for energy consumption simulation.

Material	Thickness	Conductivity	Density	Specific Heat	Roughness
Veneer	0.01 m	0.69 W/m·K	1858 Kg/m3	836 J/kg·K	Rough
Aluminum silicate cotton	0.12 m	0.05 W/m·K	120 Kg/m3	1220 J/kg·K	Very rough
Concrete	0.22 m	0.80 W/m·K	1800 Kg/m3	1250 J/kg·K	Smooth
Tile	0.005 m	0.69 W/m·K	1050 Kg/m3	1000 J/kg·K	Medium rough
Insulation	0.12 m	0.039 W/m·K	100 Kg/m3	1300 J/kg·K	Rough

and

Table 4. Transparent material properties used for energy consumption simulation.

Material	Thickness	Transmittance	Reflectance	Infrared Transmittance	Conductivity
Clean glass	0.006 m	0.85	0.071	0	0.85 W/m·K
Low-e glass	0.006 m	0.6	0.06	0	0.85 W/m·K

provide the settings for other building parameters. Due to equipment limitations, the light environment model and energy model are estimated and set based on actual conditions, and after verification, the test results and simulation results are within the allowable error range. Additionally, other simulation settings have been established by the requirements for office spaces outlined in the General Specifications for Energy Saving and Renewable Energy Use [38].

2.4. Working Principle of Center-Mounted Louver Ventilation Window

This study proposes innovative ventilation strategies designed for seasonal variations. The strategies include external circulation, internal circulation, and diagonal ventilation, etc. In summer, the external circulation ventilation strategy is preferred to effectively reduce the cooling load of the building. In winter, it mainly relies on the internal circulation ventilation strategy to reduce heat loss. For the transition season, diagonal ventilation was implemented to optimize indoor air. Theoretically, this multifaceted ventilation control is expected to enhance building energy efficiency and thermal comfort. However, for different seasonal conditions, the optimal control strategies of the center-mounted louver ventilation window need to be further verified and improved.

As shown in Figure 6, the center-mounted louver ventilation window operates in six strategies across seasons to enhance building energy efficiency. The arrows represent the direction of airflow.

The fully open strategy (Figure 6a) is optimal for the transitional seasons of spring and autumn. During these periods, the temperature difference between the indoors and outdoors is minimal, and air quality remains good. This strategy can maximize the effect of natural ventilation and improve indoor air quality. It is identified as an ideal passive ventilation strategy.

The fully closed strategy (Figure 6b) is generally suitable for peak load periods when the cooling and heating systems are running. This strategy can build a stable enclosure system and minimize heat loss and air infiltration, effectively maintaining indoor temperature stability.

The external circulation strategy (Figure 6c) is effective for cooling during the summer. It achieves passive cooling by directing the outdoor air to eliminate the heat that accumulates indoors. This strategy not only reduces heat retained in the building structure overnight but also lowers the starting load and electricity demand of the air conditioning system the next day. So, it provides significant energy-saving benefits.

The internal circulation strategy (Figure 6d) is used in cold seasons. Controlling the heat accumulated in the cavity exchange with outdoor cold air can be increased to maintain the indoor heat balance. It prevents heat loss caused by cold air infiltration, improves the operating efficiency of the heating system, and reduces energy consumption.

The inside-up, outside-down opening strategy (Figure 6e) is suitable for spring and summer. This strategy uses the thermal buoyancy effect of air: warm indoor air rises due to its lower density and is discharged through the upper opening. Relatively cool outdoor air enters from the lower opening, forming natural cross ventilation.

The inside-down, outside-up opening strategy (Figure 6f) is designed for autumn and winter. It provides adequate ventilation while minimizing the exposure of occupants to cold winds. Additionally, when solar radiation is strong, this strategy harnesses solar heat to warm the indoor environment.

The center-mounted louver ventilation window provides various passive ventilation strategies. However, its effectiveness is influenced by local climate. Therefore, optimizing it requires adjusting ventilation strategies based on climate data, building characteristics, and usage requirements. An appropriate strategy is crucial for ensuring optimal energy savings throughout the year.

Adding adjustable blinds to ventilation windows maximizes the use of passive designs to optimize system performance as shown in Figure 7. Blinds can control the daylighting and thermal environment by adjusting the number, angle, and width of three parameters. The number of blinds determines shading density and can be customized for different orientations and seasons. Adjusting the angle provides control over daylighting, ranging from full shading to partial daylighting transmission. The width adjustment balances daylighting and shading while maintaining structural stability. Multiple parameters are adjusted to ensure the optimal operation of the system under different climatic conditions and usage requirements.

The system uses the principles of natural ventilation and solar radiation to improve the building. It carries this out by adapting ventilation strategies in response to seasonal and climatic variations. Natural winds are maximized in each strategy, from heat dissipation in summer to heat retention in winter and temperature regulation during transitional seasons. The reliance on conditioning systems is reduced. Energy efficiency in the building is improved through this innovative design. Sustainable building is promoted, aligning with the long-term goals of energy-saving and low-carbon development.

2.5. Objectives and Metrics

By considering evaluation indicators, the energy-saving effect of a building can be evaluated comprehensively, ensuring that it achieves a good balance between improving building performance, increasing comfort, and reducing energy consumption.

2.5.1. Daylighting

Multiple metrics are used to evaluate indoor daylighting, with illuminance emerging as the most prevalent metric according to literature reviews. The metric describes daylighting performance by quantifying the illumination required for task execution, typically measured on a horizontal surface. Daylighting factor (DF) is a metric and easy to evaluate. It is calculated by dividing indoor and outdoor illuminance under cloudy conditions. However, DF has the limitation that it does not change according to climatic conditions, location, and orientation, which means that many daylighting problems cannot be detected [39]. In the evaluation of indoor daylighting, horizontal illuminance on the working plane is usually used as the basis for evaluation indicators, such as daylighting autonomy (DA), continuous daylighting autonomy (cDA), and spatial daylighting autonomy (sDA) [40]. Although these indicators can measure daylighting data, they only consider the minimum daylighting requirements and do not consider glare and other optical problems.

Table 5. The advantages and limitations of the daylighting evaluation metrics.

Name	Definition	Advantage	Limitation
DF (daylight factor)	The ratio of the light level inside a structure to the daylighting level outside the structure.	Easy to understand and calculate.	It does not take into account the variable sky radiation, solar altitude angle, and direction in actual situations. It only emphasizes the quantity of daylighting but does not consider the quality.
DA (daylight autonomy)	A daylight availability metric that corresponds to the percentage of the occupied time when the target illuminance at a point in a space is met by daylighting.	Taking into account the time changes and the minimum lighting requirements of users.	The calculation process is more complicated than DF. It only considers the minimum illumination and does not consider the visual discomfort caused by excessive daylighting.
cDA (continuous daylight autonomy)	On the basis of DA, consider the percentage of target illumination that daylighting can achieve when the daylighting illumination at the test point is lower than the target illumination.	It takes into account the time change and the minimum daylighting requirements of human vision. Compared with sDA and DA, cDA takes into account the continuity of light changes and more accurately reflects the changes in indoor daylighting.	Requires more complex calculation methods and software support, and the calculation process is more complicated.
sDA (spatial daylight autonomy)	The percentage of space at a test point that achieves a specific daylighting level for a specific percentage of time, using natural lighting alone, over a given period of time.	The minimum daylighting requirements for time of day and human vision are taken into account.	The calculation process is more complicated than DF. It only considers the minimum illumination and does not consider the visual discomfort caused by excessive daylighting.
UDI (useful daylight illuminance)	A daylight availability metric that corresponds to the percentage of the occupied time when a target range of illuminances at a point in a space is met by daylight.	The design takes into account the changes in time and the human visual demand for lighting, as well as the visual discomfort caused by excessive lighting.	More complex calculation methods and software support are required, and the calculation process is relatively cumbersome; the useful illumination range needs to be determined according to actual conditions; otherwise, the results may not accurately represent the lighting conditions.

is the advantages and limitations of the daylighting evaluation metrics.

Finally, useful daylighting illuminance (UDI) can be used to obtain assessment of indoor daylighting [41]. It divides the illuminance into three parts: below the target range, consistent with the target range, and above the target range. Based on the summary of previous studies by Mardaljevic et al., when the illuminance on the work plane is less than 100 lux, basic visual activities will become difficult, and when the illuminance is higher than 3000 lux, glare will occur in most cases [42]. By comparing the distribution of simplified glare indicators (DGPs) with the illuminance value, the useful illuminance target range is set to 100–3000 lux. The range is further subdivided into two parts: the supplementary useful illuminance range of 100–300 lx and the autonomous useful illuminance range of 300–3000 lx. The test points are evenly distributed on a 1 m × 1 m grid and are located at a height of 0.75 m above the working plane that meets the architectural daylighting design standards. This follows the requirements of section 5.3.2 of the Standard for Lighting Design of Buildings GB50034-2013 for the standard value of office building lighting [43]. This setting meets the Qingdao building lighting standards and can be widely applied to the design needs of office buildings across the country. Therefore, the grid test points and work surface heights selected in this study have a normative basis, which can ensure the rationality of the research results. Radiance is used to predict the annual illuminance of each test point hour by hour, and the proportion of occupied time that meets the illuminance within the specified range is calculated as UDIlow (<100 lux), UDIsup (≥100, <300 lux), UDIauto (300–3000 lux), and UDIup (>3000 lux) to characterize the indoor daylighting conditions. By calculating the proportion of time that the illuminance falls within each UDI range throughout the year, the daylighting conditions of the room throughout the year can be described. For example, the proportion of the UDIauto range reflects suitable daylighting conditions, while the proportion of UDIlow or UDIup indicates insufficient or excessive daylighting. Based on these results, targeted design recommendations can be made, such as adjusting the angle, width, or position of the blinds to optimize the daylighting effect. The calculation formula is as follows, where $\mathrm{t}\mathrm{i}$ represents each occupied hour in the calculation time, $\mathrm{f}\mathrm{i}$ is a weighting factor, and $\mathrm{E}\mathrm{i}$ represents the illuminance value of each hour.

$$\mathrm{U}\mathrm{D}\mathrm{I}={\displaystyle \frac{{\sum }_{\mathrm{i}}\mathrm{f}\mathrm{i}\times \mathrm{t}\mathrm{i}}{{\sum }_{\mathrm{i}}\mathrm{t}\mathrm{i}}}\in [0,1]\left\{\begin{array}{c}\mathrm{U}\mathrm{D}\mathrm{I}\mathrm{l}\mathrm{o}\mathrm{w}:\mathrm{f}\mathrm{i}=\left\{\begin{array}{c}1 \mathrm{E}\mathrm{i}<100\\ 0 \mathrm{E}\mathrm{i}\ge 100\end{array}\right.\\ \mathrm{U}\mathrm{D}\mathrm{I}\mathrm{s}\mathrm{u}\mathrm{p}:\mathrm{f}\mathrm{i}=\left\{\begin{array}{c}1 100\le \mathrm{E}\mathrm{i}<300\\ 0 \mathrm{E}\mathrm{i}<100,\mathrm{E}\mathrm{i}>300\end{array}\right.\\ \mathrm{U}\mathrm{D}\mathrm{I}\mathrm{a}\mathrm{u}\mathrm{t}\mathrm{o}:\mathrm{f}\mathrm{i}=\left\{\begin{array}{c}1 300\le \mathrm{E}\mathrm{i}\le 3000\\ 0 \mathrm{E}\mathrm{i}<300,\mathrm{E}\mathrm{i}>3000\end{array}\right.\\ \mathrm{U}\mathrm{D}\mathrm{I}\mathrm{u}\mathrm{p}:\mathrm{f}\mathrm{i}=\left\{\begin{array}{c}1 \mathrm{E}\mathrm{i}>3000\\ 0 \mathrm{E}\mathrm{i}\le 3000\end{array}\right.\end{array}\right.$$

(1)

2.5.2. Energy

To incorporate building energy performance into the optimization approach, it is helpful to use indicators such as standardized building energy use intensity (EUI), which is increasingly used in building performance analysis [44]. EUI is calculated by dividing a building’s total annual energy consumption by its total floor area [45]. Many internal and external factors, such as weather conditions, heating or cooling loads, and usage time, will affect this energy indicator. As a mature indicator, EUI considers all relevant energy use parameters, including annual heating, cooling, and artificial lighting loads, and is therefore widely used in the optimization assessment of energy consumption. Its calculation formula is as follows, where $\mathrm{P}$ is the energy efficiency of the building, $\mathrm{E}\mathrm{a}$ represents the energy consumption intensity of the building under design conditions, and $\mathrm{E}\mathrm{b}$ represents the energy consumption intensity of the building under reference conditions.

$$\mathrm{P}=(1-\mathrm{E}\mathrm{a}/\mathrm{E}\mathrm{b})\times 100\mathrm{\%}$$

(2)

3. Results

3.1. Model Validation

To evaluate the accuracy of the model, this research used several statistical indicators, including mean error (MBE), root-mean-square error (RMSE), and root-mean-square error coefficient of variation (CV(RMSE)). This evaluation method is consistent with the method used in previous similar studies [46,47,48,49,50,51,52]. A lower error value indicates that the model is well calibrated and effectively reduces the difference between the predicted data and the measured data.

$$\mathrm{M}\mathrm{B}\mathrm{E}=\begin{array}{c}{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}} \left(\mathrm{M}\mathrm{i}-\mathrm{S}\mathrm{i}\right)}{{\sum }_{\mathrm{i}=1}^{\mathrm{N}} \mathrm{M}\mathrm{i}}}\end{array}\left(\mathrm{\%}\right)$$

(3)

$$\mathrm{C}\mathrm{V}\left(\mathrm{R}\mathrm{M}\mathrm{S}\mathrm{E}\right)={\displaystyle \frac{1}{\mathrm{ȳ}}}\sqrt{{\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{N}} {\left(\mathrm{M}\mathrm{i}-\mathrm{S}\mathrm{i}\right)}^2}{\mathrm{n}}}}\left(\mathrm{\%}\right)$$

(4)

where n is the total amount of data used for validation, Mi and Si are the measured and simulated data in the time interval, and ȳ is the average value of the measured data. According to ASHRAE Guideline 14-2014 (Guideline 2014) [53], especially when analyzing hourly data, model calibration should meet certain criteria. Specifically, if the model’s mean bias error (MBE) is within ±10% and the root-mean-square error (CV(RMSE)) is within ±30%, the calibration is considered successful.

3.1.1. Temperature

To calibrate the baseline model, the period from November 9 to 10 was selected. The temperature inside the office was measured using Testo 174 temperature sensors, as depicted in Figure 8. All the Testo 174 temperature sensors tested were calibrated to ensure accuracy before testing.

The room was unoccupied during the test, with no lighting, electrical equipment, or HVAC systems in operation. All indoor blinds were drawn, and the doors and windows were closed during the test. The DAVIS Vantage Pro2 weather station, installed on the building’s roof, measured outdoor air temperature, humidity, and solar radiation. Model calibration data were obtained from the test results, illustrated in Figure 9. The mean bias error (MBE) for the office internal temperature was −3.96%, with a coefficient of variation of root-mean-square error (CV(RMSE)) of 5.35%. These values are within an acceptable range, indicating that the simulated temperature trend closely matches the measured data. Consequently, thermal simulation achieves the required accuracy, supporting further research.

3.1.2. Illuminance

Contrast verification was conducted on 16 December at 10:30 AM and 2:30 PM under varying sunny and cloudy conditions, ideal for illuminance measurement. The office was segmented into 15 test points of 2 m × 2 m, with measurement height set at 0.78 m. The UNI-T UT382 illuminance sensor measured illuminance at designated test points as shown in Figure 10. All the UNI-T UT382 Illuminance sensors tested were calibrated to ensure accuracy before testing.

Table 6. Illumination verification results.

Time	Sky		Measurement	Simulation	MBE	RMSE	Table
10:30					−1.14%	13.67%
14:30					−6.03%	16.21%

compares measured and simulated illumination at 10:30 AM and 2:30 PM. Error calculations show an MBE and CV(RMSE) of −1.14% and 13.67% at 10:30 AM, and −6.03% and 16.21% at 2:30 PM, all within acceptable ranges. The illumination curves demonstrate alignment between measured and simulated values, validating the simulation’s accuracy for subsequent research phases.

3.2. The Influence of Center-Mounted Louver Ventilation Window on Daylighting

3.2.1. The Daylighting Potential of the Year

Optimal daylighting is achieved with a blade width of 0.03 m, 10 blades, and at 0°. With the building located in the northern hemisphere with large window openings, the baseline model achieves an average useful daylighting illuminance (UDI) of 76.96% as shown in

Table 7. Comparison of daylighting between the baseline model and the center-mounted louver ventilation window.

	Baseline Room	Center-Mounted Louver Ventilation Window
Distribution Map of UDI(%) UDIlow UDIsup UDIauto UDIup
Average UDIlow	9.42%	16.31%
Average UDIsup	17.06%	35.26%
Average UDIauto	59.9%	43.21%
Average UDIup	13.62%	4.23%

.

This includes a UDI for supplement (UDIsup) of 17.06% and a UDI for autonomy (UDIauto) of 59.90%. After incorporating blinds, the average UDI increases to 78.47%, slightly exceeding the baseline. The UDIsup rises to 35.26%, while the UDIauto decreases to 43.21%. These results indicate improvement in the UDI, enhancing daylighting. However, the increased UDIsup suggests reliance on artificial lighting in certain areas. Therefore, it is essential to not only improve the overall UDI but also maintain a balance between the indicators to reduce artificial lighting dependency.

The use of blinds reduces the risk of glare but at the same time increases the lack of daylighting away from the window area. This approach minimizes direct sunlight but increases artificial lighting demands. Nonetheless, the indoor illumination distribution becomes more uniform, resulting in a 1.51% increase in the average UDI.

Figure 11 shows that during winter, the UDI of the baseline model is 5–9% higher than that of the center-mounted ventilation window. This suggests that while the system offers advantages in energy consumption, its impact on indoor daylighting cannot be overlooked. Relying on a single configuration to improve UDI and reduce energy consumption is impractical, underscoring the importance of balancing and optimizing two objectives.

In summer, adjustments to the louver ventilation play a vital role. Some cases can enhance the indoor UDI, indicating the system’s ability to maintain daylighting while controlling energy consumption. A balanced approach is crucial for optimizing building performance through integrated design strategies.

3.2.2. Analysis of Daylighting Potential in Typical Summer Months

The application of blinds in July reduced UDIup and enhanced the uniformity of indoor daylighting as shown in Figure 12. However, the performance showed limitations in other aspects.

Specifically, the blinds increased the UDIlow while decreasing the UDIauto, leading to a reduction in daylighting. Under the baseline model, the average UDI reached 76.96% indoors. Although blinds effectively limit excessive daylighting, they may reduce the energy benefits derived from solar radiation. Compared to the baseline model, the blinds regulate overexposure to sunlight but also reduce the utilization of daylighting, a trend evident during summer. Consequently, if daylighting is prioritized without considering energy consumption, the sun shading provided by the windows and the wall depth in the baseline model can meet the required illumination.

In summary, although blinds can control overexposure in July, the negative impact underscores the need to balance shading and daylighting. When energy savings and daylighting are both goals, the impact of shading effects on daylighting must be considered.

3.2.3. Analysis of Daylighting Potential in Typical Winter Months

Winter shows a different situation from summer as shown in Figure 13.

All simulated UDIauto results were lower than 50% of the UDI of the baseline model in January. The impact of blinds on daylighting during winter is demonstrated, leading to daylighting that is markedly inferior compared to that of the baseline model. Additionally, data from UDIlow reveals a median simulation result of 32.5%, higher than the baseline model’s 21%. This shows that after adding the blinds, the insufficient daylighting poses a challenge to indoor daylighting in winter.

Compared with the baseline model, the UDIup of the system is reduced, and the dispersion of the data is small, showing strong stability. In the baseline model, the UDIup was 22%, while the median value from the center-mounted ventilation window was just 5%. By reducing the uncomfortable daylighting, the blinds help prevent the adverse effects of excessive daylighting. The capacity to mitigate overexposure is advantageous during the winter, highlighting the blinds’ capability to adjust daylighting to the requirements while preserving visual comfort.

3.3. The Influence of Center-Mounted Louver Ventilation Window on Energy Saving

3.3.1. Annual Energy Consumption Potential

The center-mounted louver ventilation window was installed and operated for a year to evaluate its performance compared to that of the baseline model.

As shown in Figure 14, the building’s energy consumption exhibits seasonal variations. The red box signifies that an enlarged image is available. During the summer months (June to August), energy consumption shows an upward trend, with values ranging from 5 to 23 kWh/m². The data dispersion observed during this period indicates a sensitivity of the system to the external environment. In contrast, energy consumption during the winter months (December to February) is different. The values remain stable, ranging from 2.5 to 5 kWh/m². This is due to the excellent thermal insulation properties of the simulated office materials. The annual energy consumption curve reveals a characteristic “W-shaped” distribution, reflecting the seasonal changes in energy use.

A comparative analysis of the baseline model and center-mounted louver ventilation window revealed performance differences. Except for July and August, the baseline model demonstrates a higher energy consumption rate compared to that of 75% of the simulation results for center-mounted louver ventilation windows. This implies that systems possess energy conservation potential throughout most of the time. The system demonstrates not only lower energy consumption but also operational stability, as indicated by the smaller data dispersion. However, during the midsummer months of July and August, this advantage is diminished. In some cases, energy consumption exceeds that of the baseline model. This result may result from higher outdoor temperatures and the reduced efficiency of natural ventilation. Future simulations will explore different ventilation strategies to evaluate the energy-saving potential.

3.3.2. Ventilation Strategy 1—Fully Open Strategy

To assess the effect of the fully open strategy of the center-mounted louver ventilation window, monthly energy consumption data were analyzed.

As shown in Figure 15, a significant increase in energy consumption was revealed during winter when the vents are fully open. The red box signifies that an enlarged image is available. Energy consumption can reach a maximum of 52.05 kWh/m², compared to that of only 5.56 kWh/m² under the baseline model, showing a nearly tenfold difference in winter. This is attributed to low outdoor temperatures, which require more indoor heating. In contrast, energy consumption under the baseline model is generally higher than when the vents are fully open during the summer. This is because louver ventilation windows effectively utilize natural ventilation for cooling. Additionally, the blinds block direct solar radiation to reduce cooling loads. The effects of natural ventilation and shading result in energy savings in a fully open strategy during warmer months.

The analysis reveals that the main factor influencing energy use is the change in heating load. When the vents are fully opened, additional heating demand arises in all months except June to September, when heating is not required. During winter (November to March), this effect is pronounced, as fully opening the vents causes an increase in heating energy consumption. This increase results from the rapid exchange of indoor and outdoor air, which reduces the building’s insulation effectiveness.

The energy consumption for cooling and lighting in most months is lower than that of the baseline model, demonstrating the energy-saving advantages of the fully open strategy. The decrease in cooling load from May to September drives the reduction in energy consumption. The improvement is attributed to the passive cooling effects of natural ventilation and the daylighting regulation provided by the blinds, which reduce the reliance on cooling. The comparison between the fully open strategy and the baseline model indicates that this ventilation strategy is better suited for summer.

3.3.3. Ventilation Strategy 2—Fully Closed Strategy

In order to evaluate the impact of the fully closed strategy on energy consumption, an analysis of annual energy consumption was conducted.

As shown in Figure 16, during autumn and winter (September to February) there are reductions in energy consumption compared to the baseline model. The red box signifies that an enlarged image is available. This reduction is attributed to lower outdoor temperatures in winter, necessitating higher indoor heating. When the vents are fully closed, the cavity acts like a greenhouse, effectively aiding in heat retention. During winter (December to February), energy consumption under the baseline model is 2.97 kWh/m², 5.56 kWh/m², and 3.39 kWh/m². In contrast, the fully closed strategy shows lower average energy consumption values of 2.68 kWh/m², 4.32 kWh/m², and 2.97 kWh/m² during the same period. However, in the summer months of July and August, the baseline model exhibits lower energy consumption compared to that of the fully closed strategy.

The primary factor influencing changes in energy use, as revealed by monthly energy consumption, is the heating load during colder months, though cooling loads remain significant in warmer months. During the high temperatures of July and August, the fully closed strategy leads to a notable degradation in the performance of the room, causing energy consumption to surpass that of the baseline model. This situation arises because heat accumulates in the cavity and cannot be expelled, resulting in an increased cooling load for the building. In terms of heating and lighting, the fully closed strategy demonstrates a slight energy-saving advantage for most months, except during the hot summer months.

Overall, the comparison between the fully closed strategy and baseline model indicates that this ventilation strategy is particularly advantageous in winter.

3.3.4. Ventilation Strategy 3—External Circulation Strategy

The performance evaluation of the external circulation strategy in a center-mounted louver ventilation system revealed notable seasonal variations in energy savings.

As shown in Figure 17, the external circulation strategy exhibited excellent energy-saving performance during summer and transitional seasons, with energy consumption lower than that of the baseline model. The red box signifies that an enlarged image is available. The data dispersion highlights the stability of this strategy. However, this advantage diminishes significantly in winter, particularly in January, when energy consumption increases.

A detailed analysis of the composition of energy consumption indicates that the cooling load is the key factor affecting overall energy usage. This phenomenon can be explained by thermal engineering principles: strong solar radiation during the summer causes heat to accumulate in the building’s cavity. The external circulation strategy blocks the direct penetration of outdoor hot air by closing the inner vents exploiting the chimney effect to promote heat discharge from the cavity. This strategy exhibits a significant cooling effect from May to October, reducing the heat load transferred to the room and decreasing the energy consumption of the cooling.

Regarding heating and lighting, energy consumption in most months is lower than that of the baseline model, indicating that the strategy offers energy-saving advantages. The increase in total energy consumption observed from December to March is mainly due to a rise in the cooling load. This is because the ventilation strategy reduces the building’s thermal insulation performance in winter, reaching the peak energy consumption value in the coldest month, January.

As the energy performance of both the fully open strategy and the external circulation strategy in summer surpasses that of the baseline model, July has been selected as the representative summer month. A comparison chart of energy consumption under various conditions is presented to identify the optimal ventilation strategy.

Figure 18 compares the energy consumption of different ventilation strategies during July. It shows that when the windows remain closed, the room acts like a greenhouse. Consequently, significant cooling is required to maintain comfort, leading to energy consumption reaching 15.845 kWh/m². In contrast, when all the vents are opened, outdoor air enters the room. However, due to the hot summer temperatures, the air also brings heat from outside, causing the indoor temperature to rise. The result indicates that the full-open strategy has the highest energy consumption of the three strategies, totaling 17.879 kWh/m². The external circulation is the most efficient, with the lowest energy consumption at 15.335 kWh/m². This efficiency is attributed to the strong solar radiation during summer, which heats the air in the cavity. By keeping the inner vents closed to prevent hot air from entering and opening the outer vents, the chimney effect can be utilized to expel heat from the cavity. As a result, indoor cooling energy consumption is significantly reduced. For the simulated office, adopting the external circulation strategy results in energy savings of approximately 0.51 kWh/m² compared to the baseline room. This corresponds to a total reduction of 36.21 kWh for the entire space, highlighting the strategy’s significant energy-saving potential.

In conclusion, during summer, using the external circulation strategy can significantly lower indoor cooling energy consumption compared to the fully open strategy.

3.3.5. Ventilation Strategy 4—Internal Circulation Strategy

When the external vents of the center-mounted louver ventilation window are closed and the internal vents are open, internal circulation forms due to the theoretically generated thermal pressure effect. Figure 19 shows the comparison of energy consumption between the annual internal circulation strategy and the baseline. The red box signifies that an enlarged image is available.

Identifying the optimal time for this strategy to improve energy efficiency is crucial. By comparing EUI with the baseline model, we found that energy consumption in the internal circulation strategy is similar to that in the fully closed strategy. Both strategies focus on reducing heating energy in winter. So, we selected a typical winter day to compare indoor temperatures under different ventilation strategies.

The results indicate that the internal circulation strategy can increase indoor temperatures by 1–2 °C compared to the strategy with all vents closed as shown in Figure 20. This increase is attributed to specific advantages. During winter days, when sunlight directly hits the windows, the heat generated by solar radiation is stored in the cavity. At night, the warm air rises and is released indoors through the upper vents, which raises the indoor temperature and reduces the need for heating energy.

Further time series analysis shows that internal circulation ventilation is particularly effective during the period of maximum temperature fluctuation (10:00 AM to 4:00 PM), coinciding with the peak solar radiation and highest outdoor temperatures. It becomes clear that this ventilation strategy surpasses the fully closed strategy in performance during winter.

3.3.6. Ventilation Strategy 5—Inside-Up, Outside-Down Opening Strategy

The cross-ventilation strategy of the center-mounted louver ventilation window, with the inside-up and outside-down vents open, shows varying effectiveness across seasons. Figure 21 shows the comparison of energy consumption between the annual internal circulation strategy and the baseline. The red box signifies that an enlarged image is available.

Energy consumption is reduced during the summer months (June to August) as well as during the transitional seasons of spring and autumn. This strategy of opening the inside vent upward and the outside vent downward demonstrates excellent energy-saving benefits. Figure 21 indicates a small degree of dispersion during the transitional season of spring, suggesting that the strategy is relatively stable in reducing energy consumption during this period. However, this advantage changes notably in winter, particularly from November to March, when there is a significant increase in energy consumption.

When comparing cross-ventilation (Figure 22a) with the fully open strategy (Figure 22b), we discovered that cross-ventilation allows for real-time natural airflow indoors, significantly enhancing air quality and comfort. However, its cooling effect is not as pronounced as that of the fully open strategy. This is primarily because the fully open strategy enables greater air circulation, which removes heat from the room more efficiently, resulting in better cooling effectiveness. In contrast, while cross ventilation improves indoor ventilation by bringing in natural wind, its cooling rate is relatively low due to limitations in the ventilation paths and wind speed. This limitation becomes more evident during hot weather conditions, further restricting its cooling efficiency.

Figure 23 illustrates the specific energy consumption values for both ventilation modes. In May, energy consumption when unventilated was 7.43 kWh/m², while the always-on (fully open strategy) consumed 5.60 kWh/m². The energy consumption for cross ventilation was slightly higher at 5.79 kWh/m².

The data analysis indicates that the complex pathways and bends may cause greater flow resistance, reducing ventilation efficiency. Additionally, in late spring and early summer, outdoor temperatures can fluctuate significantly. The intricate airflow patterns created by cross-ventilation may disrupt the natural temperature stratification within a room, affecting the uniformity of indoor temperatures. The disturbance requires the air conditioning system to be adjusted more frequently to maintain a comfortable indoor climate, which decreases overall efficiency due to the frequent starting and stopping of the system. Although cross-ventilation complicates airflow organization, the actual effective ventilation volume may be less than that achieved through a straightforward ventilation approach. This can lead to heat accumulation and increased energy consumption for air conditioning.

This phenomenon highlights that, during transitional seasons, ventilation strategies should prioritize the simplicity and effectiveness of airflow organization and actual ventilation efficiency, rather than solely considering the complexity of ventilation systems.

3.3.7. Ventilation Strategy 6—Inside-Down, Outside-Up Opening Strategy

As shown in Figure 24, the cross-ventilation strategy of the center-mounted louver ventilation window, with the inside-down, outside-up opening strategy, shows varying effectiveness across seasons. The red box signifies that an enlarged image is available.

This strategy primarily benefits cooling during warmer months, while energy consumption during the heating season is significantly higher than in the baseline model. These seasonal differences are attributed to the natural ventilation effect and the temperature disparity between indoor and outdoor environments. In the cooling season, when outdoor temperatures rise, cross ventilation through the top and bottom openings promotes indoor airflow and enhances heat exhaust, thereby reducing the air conditioning load. Cold air enters from the bottom while hot air is expelled from the top, creating a chimney effect.

Detailed energy consumption analysis indicates that the system reduces the cooling energy usage of air conditioners from May to September, resulting in a lower overall energy consumption. However, during the heating season, this strategy of cross-ventilation can lead to increased heat loss. The top opening allows warm air to escape outside, while the bottom opening introduces cold outdoor air, which directly raises the heating system’s load.

An analysis of ventilation strategies during autumn (September to November) reveals that, although the theory suggests that the inside-down, outside-up approach may offer better ventilation effects, the actual performance in Qingdao differs from these theoretical expectations. The cooling energy consumption in Figure 25a is higher than that in Figure 25b. This discrepancy is primarily due to the seasonal lag effect typical of the region. Influenced by the maritime climate, Qingdao experiences a gradual decrease in temperature during autumn. Additionally, the thermal inertia of the building envelope leads to notable indoor heat accumulation.

As a result, indoor temperatures remain high in early autumn. Utilizing an external circulation ventilation strategy can more effectively facilitate indoor heat dissipation, ensuring occupant comfort and optimizing energy utilization. This research underscores the importance of considering regional climate characteristics and building thermal performance when optimizing ventilation strategies. It also highlights the limitations of traditional theoretical models in practical applications. Furthermore, this approach, which adjusts strategies based on actual climate characteristics, offers valuable insights for ventilation design in similar climates.

3.4. Energy Consumption and Daylighting Potential of Center-Mounted Louver Ventilation Window

3.4.1. Energy Consumption Potential of Center-Mounted Louver Ventilation Window

To investigate the energy-saving potential of various ventilation strategies across different seasons, this section focuses on net energy consumption as the evaluation metric. Based on the analysis in Section 3.3, the most energy-efficient ventilation strategy for each season is identified. The discussion will then center on the annual net energy consumption of the configuration employed by the most energy saving.

Figure 26 is the comparative data of energy consumption simulation of all ventilation strategies and the baseline. Qingdao is a typical coastal city. The climate is greatly affected by the ocean and the seasonal lag is obvious. Transition seasons resemble extended periods of summer and winter. After the simulation is found, ventilation optimization strategies are implemented for each season: external circulation is used in spring and summer, while internal circulation is applied in autumn and winter. The analysis indicates that seasonally optimized ventilation strategies achieve energy savings across all seasons. Compared to the baseline annual energy consumption of 83.81 kWh/m², the optimized strategy reduces energy consumption to 55.0 kWh/m². This corresponds to an average annual energy savings of 28.81 kWh/m² per unit area, demonstrating significant energy-saving potential and practical application value.

A comparative analysis of annual energy consumption for different ventilation strategies reveals significant variations. Energy consumption in the fully open strategy reaches 220.33 kWh/m², nearly 163% higher than that of the baseline model. The inside-down and outside-up strategy also shows elevated energy usage at 183.06 kWh/m², substantially exceeding the baseline. In contrast, the inside-up, outside-down strategy and external circulation strategy demonstrate more moderate energy consumption, at 78.05 kWh/m² and 75.02 kWh/m², respectively. The lowest energy usage is observed in the fully closed strategy (63.61 kWh/m²) and the internal circulation strategy (63.79 kWh/m²). These findings explain that no single ventilation strategy can achieve optimal energy efficiency across all seasons. For instance, the fully open strategy facilitates natural ventilation during transitional seasons but results in energy waste during extreme weather conditions. Conversely, the fully closed strategy minimizes energy consumption but may break indoor air quality. These results highlight the importance of adaptive ventilation strategies to seasonal climate variations.

3.4.2. Daylighting Potential of Center-Mounted Louver Ventilation Window

Based on the analysis results of Section 3.2, this study selected a center-mounted louver ventilation window (width of 0.03 m, number of blades 15, angle of 0 degrees) as the research object, and it evaluated the impact of different strategies on indoor effective daylighting.

Figure 27 analysis indicates that the average annual UDI under the six ventilation strategies demonstrates varying performances. The internal circulation strategy achieves the highest effectiveness, with a UDI of 78.51%. This is followed closely by the inside-up and outside-down strategy, and the inside-down and outside-up strategy, both with a UDI of 78.47% and 78.49%, respectively. These three strategies perform slightly better than the baseline model, which has a UDI of 78.01%. In contrast, the UDI for the fully open strategy, fully closed strategy, and external circulation strategy are 77.68%, 77.48%, and 77.58%, respectively. Although these values are slightly lower than those of the baseline model, the differences are not significant, and they all remain within an acceptable range. This indicates that the system demonstrates stability in maintaining the quality of daylighting.

An analysis reveals that the UDI across all the ventilation strategies consistently remains high (with each one above 77%), with a maximum fluctuation range of 77.48% to 78.51%. This finding has several reasons: First, it demonstrates that the center-mounted louver ventilation window can maintain stable daylighting performance across different ventilation strategies. Second, the internal circulation strategies’ slightly superior performance may be attributed to its airflow management, which reduces the likelihood of condensation on the glass surface, thereby enhancing the daylighting effect. Finally, even in the fully closed strategy, the system maintains a high UDI, confirming the scientific and rational design of the blinds for regulating daylighting.

4. Discussion

An innovative central louver ventilation window was proposed in this study, combined with a seasonal ventilation strategy. It reduces the building’s energy consumption while maintaining high indoor daylighting. Compared to existing research, this study has made progress in some aspects.

Firstly, in terms of ventilation strategies, H. Manz et al. investigated the ventilation performance of double-skin facade buildings. Their findings indicate that the absence of effective ventilation methods can result in elevated temperatures within the cavity and indoor spaces, subsequently increasing the air conditioning load during summer [54]. In contrast, this study reduced building energy consumption by optimizing the center-mounted louver ventilation window and adjusting the ventilation strategy to align with seasonal demands. This approach mitigates the adverse effects commonly observed in double-skin facade buildings during hot seasons. Regarding the number of glass layers, Lei Xu et al. reported that, compared to single-layer glass curtain walls, DSF buildings achieved a 10–15% reduction in cooling load and a 20–30% reduction in heating load [55]. Zerrin Yilmaz et al. also found that single-layer curtain walls and DSF have higher heat loss in winter [56]. The findings of this study further corroborate this conclusion, achieving energy savings of up to 34.4% compared to those of the baseline model. From a regional perspective, Nicola Mingotti et al. demonstrated that by appropriately adjusting the opening and closing of DSF vents, overheating in summer can be mitigated, while air can be preheated effectively in winter. This adaptive approach highlights the potential of ventilation strategies to enhance thermal comfort and energy efficiency across varying climatic conditions [57]. This study aligns with previous findings, although it did not include a regional analysis. This paper examines how temperature hysteresis affects ventilation strategies, specifically within the coastal context of Qingdao. Research indicates that temperature hysteresis in coastal areas can result in deviations from the theoretically optimal ventilation approach. Therefore, it is crucial to adjust the ventilation strategy based on the regional climate characteristics to ensure energy-saving effectiveness.

Research can be carried out around the following in the future: Firstly, we will conduct field tests on the system in different climate zones to explore the energy-saving efficiency and daylighting performance of the center-mounted louver ventilation window under different climatic conditions to demonstrate the universality and effectiveness of the proposed center-mounted louver ventilation window. Secondly, this study mainly focuses on the detailed analysis of energy consumption evaluation indicators. However, in the future, we will continue to enrich the evaluation indicators to comprehensively evaluate buildings, such as user feedback on indoor air quality and thermal comfort evaluation indicators. Thirdly, the integration of smart technologies represents a significant future development trend. Future research will explore the feasibility and performance of integrating smart controls, leveraging sensors, and algorithms to achieve adaptive and efficient system operation. Fourthly, when expanding the evaluation framework in the future, we will prioritize integrating life cycle assessment into the evaluation framework. Including a life cycle assessment of the materials used in the window system ensures sustainability throughout its lifespan. Lastly, the potential for combining this system with other passive design strategies in the future will be investigated to create a more comprehensive approach to energy-efficient building design without disrupting the building’s core.

5. Conclusions

The comprehensive performance of center-mounted louver ventilation window was evaluated in this study, focusing on building energy conservation and daylighting. Based on the findings, the following main conclusions were drawn:

• In terms of energy efficiency, the seasonal ventilation strategies and parameters of blinds were optimized to significantly improve the energy efficiency of the building. The optimized solution reduces energy consumption from 83.81 kWh/m² to 55.0 kWh/m², achieving a 34.4% reduction. It indicates that no ventilation or a single ventilation strategy is difficult to adapt to needs throughout the year, and the ventilation strategy needs to be flexibly adjusted according to seasonal changes.
• In terms of daylighting, the system shows stability characteristics. The indoor effective daylighting under different ventilation strategies remains at a high level, with a fluctuation range of 77.48–78.51%. The internal circulation strategy performs best in terms of daylighting effect (78.51%), which is slightly better than the baseline model (78.01%).
• Different ventilation strategies show obvious differences in seasonal adaptability: The external circulation strategy is most effective in summer by blocking hot air infiltration and using the chimney effect to remove cavity heat. The internal circulation strategy performs best in winter and can effectively use solar radiation to raise the indoor temperature by 1–2 °C. The transition season needs to consider the regional climate, especially the influence of the seasonal lag effect, and flexibly adjust the operation strategy.
• Regional climate characteristics have an important impact on system performance. This study found that in Qingdao, due to its coastal location, the climate has oceanic characteristics. The thermal inertia effect of the building and seasonal lag of the building envelope will affect the actual effect of the ventilation strategy and need to be specially considered in operation optimization.

This study explores the effects of an innovative center-mounted louver ventilation window on energy consumption and daylighting in office buildings under different seasonal ventilation strategies. The research results show that with the support of different ventilation strategies, the system can effectively reduce building energy consumption by utilizing natural ventilation, showing significant energy-saving potential. More research on this topic will be conducted in the future and provide an important reference for developing sustainable buildings.