Dynamic Integration of Shading and Ventilation: Novel Quantitative Insights into Building Performance Optimization

Abstract

Buildings consume nearly 40% of global energy, necessitating innovative strategies to balance energy efficiency and occupant comfort. While shading and ventilation are critical to sustainable design, they are often studied independently, leaving gaps in understanding their combined potential. This study provides a novel quantitative analysis of dynamic shading and ventilation strategies, using a dataset of 5000 simulations in IDA Indoor Climate and Energy (IDA ICE) to reveal the synergies and trade-offs in building performance. Four distinct scenarios are analyzed: minimal shading and limited ventilation (shading factor $S_f$ = 0.0, ACH = 0.5), optimized shading and moderate ventilation ($S_f$ = 0.5, ACH = 1.5), dynamic shading and enhanced ventilation ($S_f$ dynamically adjusted, ACH = 2.5), and high shading with maximum ventilation ($S_f$ = 1.0, ACH = 3.0). The results show a progressive reduction in thermal discomfort, with the predicted percentage dissatisfied (PPD) decreasing from >80% in the first scenario to ~25% in the dynamic scenario and ~15% in the high shading scenario. The energy demand increases by up to 40% in the highest shading scenario, highlighting trade-offs. These findings underscore the importance of dynamically integrating shading and ventilation, providing actionable recommendations such as dynamic shading and night cooling that can reduce discomfort and improve energy efficiency by up to 30%. By bridging the research gaps, this study advances sustainable building design and offers a robust framework for creating energy-efficient, comfortable buildings.

1. Introduction

The building sector is a significant global energy consumer, accounting for nearly 40% of the total energy use, and a major contributor to carbon emissions [1,2,3]. As energy demands increase and climate challenges intensify, creating buildings that are both energy-efficient and comfortable for occupants has become a crucial focus for architects, engineers, and policymakers [2,4,5,6,7]. Achieving this balance requires a detailed understanding of the key factors affecting energy performance and indoor environmental quality. Among these, shading systems and ventilation strategies are two critical elements that strongly influence a building’s performance [8].

Shading devices, including overhangs, louvers, and dynamic blinds, regulate the solar radiation entering indoor spaces [8]. Solar heat gain directly impacts heat extraction rates, indoor temperatures, and visual comfort, particularly in regions where overheating is a concern [9]. Proper shading reduces reliance on active cooling systems, improves daylight availability, and supports energy conservation [10]. In addition to traditional shading devices like overhangs, louvers, and dynamic blinds, there are several other shading measures that can significantly contribute to a building’s energy efficiency and occupant comfort. Additional measures such as external louvers, perforated screens, vegetative shading, reflective films, solar reflective roofs, and brise-soleil expand the toolkit for optimizing building energy performance and indoor environmental quality. External louvers provide tailored obstruction of sunlight through adjustable slats, adaptable to specific solar exposures and seasonal changes. Perforated screens offer a mesh-like structure that permits airflow while blocking direct sunlight, mitigating glare and contributing to passive cooling. Vegetative shading leverages the natural properties of plants to create a dynamic and aesthetically pleasing shading solution, enhancing both comfort and sustainability through evapotranspiration and thermal mass effects. Reflective films and coatings on windows serve as an efficient means of reducing solar heat gain by reflecting incoming sunlight, thereby decreasing reliance on active cooling systems. Lastly, solar reflective roofs, characterized by high solar reflectance materials, lower roof surface temperatures and reduce the heat transfer into the building. Brise-soleil, which are architectural shading devices inspired by natural forms and patterns, offer visual interest alongside their functional benefits, optimizing solar control according to the building’s orientation and local climate. The effectiveness of shading systems depends on their material properties, geometry, and integration with other design elements [11]. Studies show that optimal shading configurations can achieve energy savings of up to 38% [12]. Furthermore, integrated shading and natural ventilation approaches can reduce the cooling and heating energy demand by approximately 20% and 5.6%, respectively, while decreasing the overheating hours by up to 75 [13].

Ventilation systems play an essential role in maintaining indoor air quality and regulating thermal conditions [14]. Strategies such as natural ventilation, mechanical ventilation, and hybrid systems help create comfortable environments while addressing energy efficiency by providing their unique advantages in different scenarios. Natural ventilation reduces the energy demand in mild climates by utilizing wind or buoyancy-driven airflow; mechanical ventilation ensures optimal air quality and temperature control in extreme or polluted environments; and hybrid systems combine the strengths of both, dynamically adapting to seasonal or climatic variations to balance occupant comfort and energy savings [15]. Night cooling, a targeted ventilation strategy, utilizes the naturally cooler nighttime temperatures to pre-cool indoor spaces, which not only reduces the daytime heat extraction rates but also enhances thermal comfort within the building. This approach has demonstrated significant efficiency, especially in moderate and cold climates where there is a substantial diurnal temperature variation, as the process effectively exploits the difference between the outdoor and indoor temperatures to cool the thermal mass of the building, which subsequently acts as a heat sink during the daytime. Research highlights that optimized ventilation rates, typically in the range of 8 to 10 air changes per hour, coupled with extended operation durations, considerably improve the performance of night cooling strategies, as evidenced by the reductions in indoor temperatures of up to 3.9 °C in buildings with heavyweight constructions [16]. However, for lightweight constructions such as office buildings in cold climates, the application of mechanical night ventilation can still yield substantial energy savings when combined with appropriately tailored control strategies, such as precise timing of ventilation operation and regulation of airflow rates, ultimately achieving energy savings of up to 25% while ensuring indoor comfort [16]. The success of ventilation approaches is influenced by the airflow rates, temperature differences, and compatibility with passive design features. Experimental studies demonstrate that these strategies can enhance occupant comfort and lower energy consumption by up to 95% in naturally ventilated spaces [17].

2. Literature Review

This section provides a detailed examination of the key components influencing sustainable building performance. It begins with an analysis of shading systems, followed by a discussion on ventilation strategies, before addressing their combined effects and the role of predictive modeling in optimizing performance.

2.1. Shading Systems and Solar Control

Shading systems are vital components of sustainable building design, serving to control solar heat gain and enhance energy performance [18]. Various types of shading systems, such as fixed overhangs, vertical louvers, and dynamic devices like motorized blinds, have been studied for their ability to improve thermal and visual comfort [19]. Fixed shading devices are effective in reducing overheating in predictable conditions, while dynamic systems adapt to changing solar angles and weather conditions, offering flexibility and greater efficiency [20]. In a simulation-based study, shading systems were shown to decrease annual energy consumption by 20–25%, particularly when integrated with adaptive control mechanisms in climates with high solar exposure [21].

The effectiveness of shading depends on multiple factors, including the device’s geometry, orientation, and material properties [22]. Studies emphasize the need for climate-specific shading designs, as the solar gain dynamics vary significantly across different regions [23]. In hot and arid climates, external shading devices, by intercepting and reflecting solar radiation before it penetrates the building envelope, significantly reduce heat gains, enhance thermal comfort, and minimize energy consumption for cooling. Research highlights that these devices are especially effective when combined with optimal glazing systems, as they maintain visual comfort by reducing glare and daylight penetration variability [24]. Additionally, external shading systems outperform internal alternatives in reducing primary energy consumption, as they limit cooling requirements while maintaining a balance with lighting needs by controlling solar transmittance and reflectance properties [24]. In contrast, in temperate climates, carefully designed shading systems can balance daylight access with thermal regulation, improving both comfort and energy efficiency.

Kinetic shading systems, such as the bi-sectional horizontal fin system studied by Brzezicki (2024) [25], dynamically respond to climatic conditions to balance daylight access and thermal regulation. These systems are particularly effective in temperate climates, where seasonal and daily variations in the solar angles necessitate flexible shading solutions. The study highlights that such systems improve visual comfort by reducing glare and excessive solar heat gain, especially during peak solar periods. Simulations reveal that the effectiveness of these systems varies with the geographic location and climatic conditions, performing optimally in regions with high solar radiation yet moderate temperature fluctuations. For example, in Wroclaw, the kinetic system achieved an 88.96% median useful daylight illuminance, showcasing its adaptability in providing thermal and visual comfort. This adaptability makes kinetic shading systems suitable for diverse architectural contexts, particularly where energy efficiency and occupant comfort are key design objectives [25].

Advanced shading technologies, such as electrochromic glazing and automated blinds, have gained attention for their ability to optimize indoor conditions dynamically [26], achieving an additional 10–15% improvement in energy savings when integrated with automated controls [27]. Research highlights the integration of shading devices with glazing properties, showing that such combinations can significantly improve energy performance while maintaining acceptable levels of daylight and view [28]. While shading systems are highly effective in managing solar gains, their role must be carefully coordinated with other design elements, such as ventilation and insulation, to maximize their overall impact. While kinetic shading systems, exemplified by the bi-sectional horizontal fin system, demonstrate significant potential in enhancing both visual and thermal comfort through dynamic adaptation to varying solar conditions, it is crucial to contextualize their performance alongside other advanced shading technologies. Electrochromic glazing, though a potent solution, is not the sole innovative approach to addressing energy efficiency and occupant comfort through shading. Photochromic and thermochromic glazing technologies represent viable alternatives, each with distinct operational principles and efficacies that can vary based on the typology of windows and specific climatic conditions [29,30]. Photochromic glazing changes properties in response to UV radiation, automatically adjusting the transparency levels to modulate incoming light and heat. This adaptability is particularly beneficial in environments with fluctuating solar intensities, such as regions experiencing intense ultraviolet radiation. Conversely, thermochromic glazing responds to temperature changes, altering its optical characteristics to control the amount of heat transferred through the window [29]. This characteristic makes thermochromic glazing an optimal choice in climates with significant diurnal temperature variations, where the ability to regulate the heat gain during both cold and warm periods is essential.

Research detailed in [29,30] underscores the potential of these technologies to achieve considerable energy reductions, highlighting the importance of selecting the appropriate shading solution based on the building’s geographical location and specific design objectives. For instance, the study provides empirical evidence of photochromic glazing’s capacity to diminish internal heat under high solar exposure scenarios, reporting that thermochromic glazing can contribute to energy savings of approximately 15–20% by effectively modulating heat transfer during extreme temperature differences between the interior and exterior of a building.

In conclusion, while kinetic shading systems offer a dynamic and adaptable approach to managing solar gain, the selection of shading technology should be comprehensive and context-driven. The integration of photochromic and thermochromic glazing into building designs, complemented by an understanding of their performance under various climatic conditions and window typologies, ensures a more holistic strategy toward achieving high levels of energy efficiency and occupant comfort [29,30].

While foundational studies remain relevant for understanding long-term trends in shading and ventilation effectiveness, we acknowledge the need to incorporate more recent literature. The integration of kinetic shading systems [25] and climate-responsive shading algorithms provide contemporary insights into optimizing solar control [31]. Our study distinguishes itself by synthesizing these emerging technologies with large-scale simulation data, offering a bridge between theoretical advancements and practical implementation.

2.2. Ventilation Strategies and Thermal Regulation

Ventilation is another cornerstone of sustainable building design, essential for maintaining indoor air quality and regulating thermal conditions [32]. Different ventilation strategies, including natural ventilation, mechanical ventilation, and hybrid systems, have been investigated for their potential to reduce energy consumption while ensuring thermal comfort [33]. Natural ventilation relies on airflow through windows, vents, and openings to provide cooling and fresh air [34]. Larsen et al. (2018) provide critical insights into the calculation methods for single-sided natural ventilation, emphasizing the importance of accurate airflow prediction for designing effective ventilative cooling systems [35]. The study evaluates the updated EN 16798-7:2017 standard, which offers a more conservative approach compared to earlier methods, ensuring safer estimations of airflow rates. Through comparing the new model to wind-tunnel measurements and computational analyses, the authors demonstrate its reliability, especially in scenarios where temperature differences across openings influence the airflow. The findings highlight the model’s suitability for practical applications, particularly in climates requiring precise ventilation designs to balance energy efficiency and indoor air quality. Studies show that effective natural ventilation depends on architectural factors, such as window placement, size, and operability, as well as external climatic conditions [36].

Mechanical ventilation systems, often integrated with heating, ventilation, and air conditioning (HVAC) systems, are widely used to ensure consistent indoor conditions in various climates [37]. Research indicates that mechanical systems can be energy-intensive, but their efficiency improves when coupled with advanced controls, such as demand-driven ventilation, which can reduce energy waste by 20–30% [38,39]. These systems adjust the airflow rates based on indoor air quality metrics, reducing energy waste while maintaining occupant health and comfort.

Hybrid ventilation systems, which combine natural and mechanical approaches, have emerged as a promising solution for balancing energy efficiency with performance reliability [33]. Studies reveal that hybrid systems can reduce reliance on active cooling by up to 82% through utilizing natural ventilation during favorable conditions and switching to mechanical systems when necessary [15,17]. Night cooling, a passive ventilation technique, has also been extensively studied [40,41]. It involves pre-cooling indoor spaces during the night using cooler outdoor air, thereby reducing the daytime heat extraction rates [39]. Night cooling strategies can achieve an average reduction in energy [42] of up to 27%. This approach has been particularly effective in warm climates, where it significantly reduces energy consumption.

To further enrich the understanding and application of building ventilation, the literature review presents sophisticated, multi-scale modeling techniques that significantly enhance the comprehension of building ventilation dynamics [43]. It details the development of a CFD model, validated against experimental data [44,45], capable of simulating the airflow patterns and temperature distributions within buildings under different ventilation strategies [46,47,48]. This detailed model facilitates the examination of intricate interactions between architectural elements, climate variables, and operational conditions, offering insights that simpler methods might neglect.

Furthermore, the paper presents an analytical method grounded in dimensional analysis designed specifically to predict the performance of natural ventilation systems, particularly beneficial during the preliminary design phases. This approach simplifies the estimation of airflow rates and thermal comfort levels, providing designers with an efficient means to evaluate various ventilation scenarios without extensive computational power.

Incorporating these advanced methodologies deepens the technical knowledge of building ventilation and equips architects and engineers with powerful tools for crafting more effective and energy-efficient systems. By merging computational simulations with analytical models, the field advances toward a more predictive and holistic approach to ventilation design, ensuring optimal harmony between energy efficiency, indoor air quality, and thermal comfort across diverse climatic contexts and building typologies.

The design and operation of ventilation systems are critical for their effectiveness. Research highlights the importance of controlling airflow rates and maintaining temperature differentials to avoid overcooling or under-ventilation [49]. Additionally, the thermal mass of building materials plays a role in enhancing the effectiveness of ventilation strategies, as a high thermal mass helps to stabilize indoor temperatures, reducing the cooling demand [50].

2.3. Combined Effects of Shading and Ventilation

While shading and ventilation have been studied extensively as individual strategies, fewer investigations address their combined effects on building performance [51]. The interaction between these elements can influence thermal comfort, energy consumption, and indoor air quality in significant ways, with an energy savings potential of 25–40% in hot climates [21,52]. The psychological effects of thermal comfort play a significant role in influencing energy consumption in buildings. Studies, including those by Turhan and Özbey (2023), demonstrate that mood states affect occupants’ perception of thermal sensation. Introducing a mood state correction factor (MSCF), they find that individuals in very pessimistic or optimistic mood states tend to feel warmer, irrespective of the actual temperature. This aligns with findings that negative mood states amplify perceived thermal discomfort, potentially driving higher HVAC energy demands to achieve perceived comfort. Addressing these psychological impacts in thermal comfort models could lead to more adaptive and energy-efficient systems [53]. Recent studies emphasize that occupant behavior significantly influences energy use and thermal comfort [53]. To incorporate these effects, future iterations of this research should integrate user-controlled shading preferences and real-time adaptive comfort models into simulations.

Shading devices directly impact the thermal loads that ventilation systems must address. Well-designed shading reduces solar heat gain, which in turn decreases the need for mechanical cooling and enhances the effectiveness of natural ventilation [54].

Ventilation strategies, whether passive or mechanical, also influence indoor temperature dynamics, thereby affecting the performance of shading systems. For example, effective ventilation can lower indoor temperatures, reducing the cooling demand and enhancing the shading system’s ability to maintain comfortable conditions [55]. Conversely, poorly designed ventilation can create hotspots or uneven cooling, limiting the benefits of shading [36]. Grussa et al. (2017) explore the combined impact of shading systems and ventilation strategies on overheating in residential buildings. The study finds that poorly integrated ventilation strategies, such as inadequate window opening areas or mismatched shading devices, lead to uneven cooling and hotspots, particularly in areas where natural ventilation was restricted. This highlights the importance of addressing shading, glazing, and ventilation collectively during the design phase. Effective facade management strategies that optimize window sizes and shading devices can mitigate these issues, ensuring even cooling and reducing the need for mechanical intervention [56].

Studies exploring this interaction often focus on climate-specific solutions. In hot and arid climates, shading combined with night cooling has been shown to achieve significant energy savings while maintaining thermal comfort [57]. External shading devices prevent excessive solar radiation during the day, while night cooling dissipates the accumulated heat, stabilizing indoor temperatures [58]. In humid climates, the combination of shading with mechanical ventilation proves more effective, as it addresses both heat and moisture concerns [59,60].

The interplay between shading and ventilation is also influenced by the building orientation, occupancy patterns, and external weather conditions. Dynamic shading systems, when integrated with sensor-driven ventilation controls, show potential for real-time optimization of energy use and indoor comfort [8,61], where it can reduce peak cooling demands by up to 50% in hot–arid regions [17].

Studies such as those by Tzempelikos and Athienitis [10] and Yang et al. [62] demonstrate the effectiveness of dynamic shading systems and optimized ventilation techniques in enhancing thermal comfort [52,63] and energy efficiency [64]. Furthermore, many studies in the literature [14,65,66,67,68] explore the synergistic effects of these strategies, emphasizing their potential to reduce cooling demands and improve occupant comfort. Machine learning approaches, as highlighted by Neto and Fiorelli [69], have further advanced the ability to predict and optimize the outcomes of these design elements [70], underscoring the relevance of prior methodologies in shaping current research directions. While the authors acknowledge these prior contributions, their findings primarily reaffirm existing knowledge rather than introducing novel insights, which could have been strengthened by deeper integration of these foundational studies to refine expectations and advance the discourse on sustainable building solutions.

Research in this area is still emerging, with ongoing studies highlighting the need for integrated approaches that consider shading and ventilation as complementary strategies rather than isolated design elements. These findings emphasize the importance of holistic design methodologies in advancing sustainable and efficient building performance.

Recent post-2020 studies explore the dynamic integration of shading and ventilation, with an emphasis on adaptive controls and smart technologies. Research by Li et al. (2024) [38] highlights the effectiveness of electrochromic glazing combined with adaptive ventilation, reducing cooling loads by 35% in high solar gain environments [71]. Similarly, researchers investigate AI-driven shading and ventilation strategies, demonstrating up to 40% energy savings with sensor-based controls [72,73,74]. Our approach builds upon these advancements by providing a quantitative framework that explicitly analyzes the interaction between shading and ventilation factors across 5000 simulations, capturing a broader range of real-world climatic conditions and operational variations.

2.4. Machine Learning and Predictive Models in Building Performance

Machine learning (ML) has emerged as a transformative tool in building performance optimization, enabling detailed analysis of the complex interactions among design parameters such as shading, ventilation, and energy use [75,76]. Studies demonstrate that incorporating interaction terms between shading and ventilation in machine learning models enhances the prediction accuracy by 15–20% compared to traditional regression techniques [27]. ML models, including decision trees, random forests, and neural networks, have been applied to predict and optimize building energy performance and occupant comfort [77]. These models excel at identifying nonlinear relationships and feature interactions that traditional statistical methods, such as regression, might overlook [78]. Studies demonstrate that ML models improve the prediction accuracy significantly compared to traditional statistical methods. For instance, optimized neural networks achieve prediction accuracies of over 99% for the annual energy demand and discomfort degree-hours, outperforming alternative models such as atom search optimization and satin bowerbird algorithms [79].

In recent studies, random forests and gradient boosting methods have been used to rank the importance of shading and ventilation parameters in reducing heat extraction rates and improving indoor comfort [80]. For instance, feature importance analysis often highlights the role of variables like the shading factor and ventilation rate, confirming their critical influence on the PPD and energy consumption for cooling [81]. Clustering algorithms, such as k-means, have also been employed to group building configurations based on performance outcomes, aiding in the identification of optimal design scenarios [82]. Moreover, surrogate modeling techniques like Kriging have reduced computational times by five orders of magnitude compared to conventional energy simulation methods while maintaining prediction errors below 2% [83].

Regression models, particularly multiple linear regression, remain a valuable tool for quantifying the relationships between key variables [84]. These models provide interpretable insights into how shading and ventilation strategies contribute to energy performance [85]. Studies often incorporate interaction terms in regression analyses to capture the combined effects of shading and ventilation, enabling designers to evaluate the trade-offs between energy consumption and occupant comfort.

Moayedi et al. (2019) employ a variety of machine learning techniques, including random forests (RFs), multi-layer perceptron regressors (MLPrs), and radial basis function regression (RBFr), to predict the heating loads in energy-efficient buildings. Among these, the random forest model emerges as the most effective, achieving near-perfect predictive accuracy, with an R² value of 0.9997 for training data and 0.9989 for testing data. The study underscores RF’s capability in capturing complex nonlinear relationships and its robustness in modeling building energy performance [86]. Moreover, Roy et al. (2020) propose the use of deep neural networks (DNNs) for forecasting heating in buildings. The DNN model outperforms gradient boosting machines (GBMs), Gaussian process regression (GPR), and minimax probability machine regression (MPMR), achieving prediction accuracies of 99.76% for heat extraction rates and 99.84% for heating loads. This study highlights the suitability of DNNs for modeling highly nonlinear interactions among design parameters, providing insights into their application for enhancing energy efficiency during the design phase [87].

To conclude the literature review, it is crucial to encapsulate the state-of-the-art performance in terms of shading and ventilation strategies, highlighting the key findings and advancements across various methodologies and technologies, as summarized in

Table 1. Comparative overview of shading and ventilation performance.

Strategy/Technology	Key Findings/Performance Metrics	Reference
Fixed shading devices	Annual energy consumption reduction of 20–25% Optimal in predictable conditions	[21]
Dynamic shading systems (Bi-sectional horizontal fin system)	88.96% median useful daylight illuminance in Wroclaw Up to 82% reduction in active cooling reliance	[25]
Electrochromic glazing	Additional 10–15% energy savings with automated controls Improves energy performance while maintaining daylight and view	[26]
Photochromic glazing	Up to 43% reduction in internal heat gains under high solar exposure	[19]
Thermochromic glazing	15–20% energy savings by modulating heat transfer during temperature fluctuations	[20]
Natural ventilation	Up to 30% energy waste reduction with demand-driven ventilation controls	[39]
Hybrid ventilation systems	82% reduction in active cooling reliance through natural and mechanical integration	[15,17]
Night cooling	Up to 27% energy reduction through pre-cooling nighttime air	[42]
Advanced ventilation models (CFD, dimensional analysis)	Enhanced simulation and prediction of airflow, temperature distributions, and thermal comfort	[49,52]

.

Fixed shading devices offer an annual energy consumption reduction of 20–25% and are particularly effective in predictable conditions [21].

Dynamic shading systems, exemplified by the bi-sectional horizontal fin system, demonstrate remarkable performance, achieving 88.96% median useful daylight illuminance in Wroclaw [25] and reducing active cooling reliance by up to 82% through effective integration with mechanical systems. Electrochromic glazing not only enhances energy performance by providing an additional 10–15% energy savings when coupled with automated controls but also ensures optimal daylight and view [26]. Photochromic glazing showcases the potential for substantial internal heat gain reduction, boasting an up to 43% reduction under high solar exposure [19], while thermochromic glazing contributes to energy savings by modulating the heat transfer during temperature fluctuations, offering a 15–20% energy reduction [20]. Natural ventilation, when controlled with demand-driven strategies, can reduce energy waste by up to 30% [39], and hybrid ventilation systems achieve an impressive 82% reduction in active cooling reliance through the seamless integration of natural and mechanical methods [15,17].

Night cooling techniques, involving the pre-cooling of indoor spaces during cooler nighttime hours, have proven effective in reducing daytime heat extraction rates, resulting in up to 27% energy savings [42]. Advanced ventilation models, including computational fluid dynamics (CFD) simulations and dimensional-analysis-based analytical approaches, significantly enhance prediction capabilities in terms of airflow, temperature distributions, and thermal comfort, thereby enabling more accurate design optimizations [49,52].

Existing works have investigated various shading technologies, including electrochromic, photochromic, and thermochromic glazing, each offering different advantages in terms of the dynamic adaptation and energy efficiency gains. Moreover, advanced computational methods have enabled detailed performance modeling of natural and mechanical ventilation strategies. Previous research has shown that optimized natural ventilation can reduce cooling loads, while hybrid ventilation strategies incorporating night cooling can achieve great energy savings. However, a comprehensive assessment of how shading and ventilation dynamically interact across different environmental conditions remains limited. This study aims to bridge this gap by quantifying the interplay of these two strategies through extensive simulation-based analysis.

Weaving together these shading and ventilation technologies, tailored to specific climatic contexts and building types, presents a comprehensive and holistic strategy for achieving high levels of energy efficiency, occupant comfort, and indoor environmental quality. By further integrating these advanced methodologies and foundational studies, the future of sustainable building design will undoubtedly benefit from increasingly nuanced and effective strategies.

2.5. Integration of Machine Learning with Scenario Analysis

The integration of machine learning models with scenario-based analysis further enhances the ability to predict and optimize building performance [88]. Through training ML models on simulation datasets, researchers can uncover complex interactions between shading and ventilation strategies [76,89,90]. Predictive models can also be used to extrapolate performance outcomes for untested configurations, reducing the need for exhaustive simulations [90].

For instance, decision-tree-based models have been employed to identify the thresholds where shading and ventilation strategies are most effective [91]. Random forests and gradient boosting models excel at ranking features by importance, highlighting the role of variables such as the night cooling rate and shading factor [92]. Clustering techniques complement this analysis by grouping scenarios with similar performance characteristics, providing a clear picture of design configurations that consistently achieve energy savings and comfort improvements [93]. Validation through cross-validation and sensitivity analysis ensures the reliability of these models, offering robust insights for guiding sustainable building design [94]. This combined approach highlights the potential of data-driven methodologies to advance the understanding of shading and ventilation interactions in achieving energy-efficient and occupant-friendly buildings [95].

3. Methodology

The methodology employed in this study encompasses a structured, multi-step approach to analyze and optimize building performance, as depicted in Figure 1. The process begins with data collection, ensuring the acquisition of the comprehensive simulation data required for a robust analysis. This is followed by exploratory data analysis, where the statistical distributions, trends, and correlations among variables are examined. Parametric analysis evaluates the individual impacts of key design parameters, such as shading and ventilation, on energy performance and comfort metrics. Regression modeling is utilized to quantify the relationships between variables and to predict the outcomes based on specific configurations. Advanced machine learning models are applied to explore nonlinear interactions and assess the importance of parameters, enhancing the understanding of complex dependencies. Finally, the methodology concludes with validation and a sensitivity analysis, ensuring the reliability and accuracy of the results. This systematic approach enables the identification of optimal solutions for sustainable and energy-efficient building designs.

3.1. Data Collection

This study utilized a dataset (

Table 2. Key characteristics of the simulated room and parameters.

Parameter	Value/Range
Room dimensions	20 m2 area, 3 m ceiling height
Cooling setpoint temperature	24 °C
Heating setpoint temperature	20 °C
Relative humidity	50%
Facade U-value	0.2–1.5 W/m2 K
Thermal mass	50–200 kJ/m2 K
Window-to-wall ratio (WWR)	20–80%
Shading factors	1 (no shading) to 0 (complete shading)
Reflectance	0.50–0.75
Ventilation rates (ACH)	0.5–2 ACH
Internal heat loads	12–25 W/m2
CO2 generation	8 L/s per person
Ventilation rate per person	≤1000 ppm
Daylight factor (>2%)	4–70%
Clothing insulation (clo)	0.5
Metabolic rate (met)	1.2
Solar heat gain coefficient (SHGC)	0.35
Climate	Warm–temperate climate
Weather data	Hourly profiles of temperature, humidity, and solar radiation

) generated through 5000 simulations in IDA ICE, focusing on the performance of a single room (Figure 2) under varied design and operational conditions. The simulations utilized high-resolution weather data from the ASHRAE IWEC dataset, incorporating hourly variations in solar radiation, wind speed, and ambient temperature. The material properties, including the facade U-values (0.2–1.5 W/m² K) and glazing SHGC (0.35), were selected based on industry standards. The building envelope characteristics were modeled to align with contemporary office typologies, ensuring realistic performance assessments.

This dataset was designed to analyze critical parameters influencing energy efficiency and thermal comfort while enabling detailed exploration of shading and ventilation strategies. The key characteristics of the room, building envelope, design parameters, and operational conditions are summarized in Table 2.

The room was modeled as a 20-square-meter office with a 3 m ceiling height. The facade U-values varied from 0.2 to 1.5 W/m² K, representing different levels of insulation, while the thermal mass ranged from 50 to 200 kJ/m² K, simulating lightweight to heavyweight constructions. The shading configurations included fixed devices (overhangs and louvers) and dynamic blinds, with the shading factors ranging from 1 (no shading) to 0 (complete shading). The ventilation strategies encompassed natural ventilation, mechanical ventilation, and hybrid systems, with the air changes per hour (ACH) adjustable between 0.5 and 2. Night cooling was integrated into selected scenarios to utilize the lower nighttime temperatures for pre-cooling indoor spaces.

The internal heat loads were set between 12 and 25 W/m² to simulate occupant presence, lighting, and equipment. The ventilation rate was set at 8 L/s per person to maintain the indoor air quality (IAQ) and CO₂ concentrations below 1000 ppm, consistent with ASHRAE 62.1-2019. The thermal comfort metrics were assessed using the Fanger PMV/PPD model [96], which predicts the thermal sensation and dissatisfaction levels based on parameters such as the air temperature, humidity, mean radiant temperature, clothing insulation, and metabolic rate. This model provides a robust and widely recognized framework for evaluating occupant comfort, allowing this study to correlate shading and ventilation strategies with thermal comfort outcomes. Dynamic coupling of the shading and ventilation strategies was implemented, where higher shading factors reduced the heat extraction rates, influencing the ventilation requirements.

The simulations were conducted in a warm–temperate climate characterized by moderate seasonal variations, classified under the Köppen–Geiger system as Cfa (humid subtropical) [97]. Hourly profiles for the temperature, humidity, and solar radiation were used to reflect realistic weather conditions, ensuring the validity of the results. Table 2 summarizes all the parameters and ranges used in this study to provide a clear overview of the dataset’s scope and variability.

3.2. Data Analysis Approach

The analysis was designed to uncover both the direct and interactive effects of shading and ventilation parameters on building energy performance and thermal comfort. A comprehensive methodology was applied, combining statistical methods, parametric analysis, and machine learning techniques. Each approach contributed a unique perspective, ensuring a thorough investigation into the dynamics of shading, ventilation, and their combined impact on performance outcomes.

3.2.1. Exploratory Data Analysis

Exploratory data analysis (EDA) served as the first step in understanding the dataset. Statistical summaries, including measures of the central tendency and variability, were computed to describe the distribution of the variables. For instance, the mean ($\mu$) and standard deviation ($\sigma$) were calculated for all the key parameters to provide insights into their typical values and variation. The standard deviation [98] was defined mathematically as:

$${\sigma }_x=\sqrt{{\displaystyle \frac{1}{n}}\sum _{i=1}^n{\left(x_i-{\mu }_x\right)}^2}$$

(1)

where $x_i$ represents individual observations, $n$ is the total number of data points, and ${\mu }_x$ is the mean value:

$${\mu }_x={\displaystyle \frac{1}{n}}\sum _{i=1}^n{x}_i$$

(2)

In addition to descriptive statistics, correlation analysis was used to evaluate the linear relationships between pairs of variables. For example, the correlations between the shading factors and the heat extraction rates were assessed to understand their impact on energy efficiency.

The correlation coefficient ($r$) was calculated as:

$$r_{xy}={\displaystyle \frac{{\sum }_{i=1}^n\left(x_i-{\mu }_x\right)\left(y_i-{\mu }_y\right)}{\sqrt{{\sum }_{i=1}^n{\left(x_i-{\mu }_x\right)}^2{\sum }_{i=1}^n{\left(y_i-{\mu }_y\right)}^2}}}$$

(3)

These correlations were visualized through scatter plots and heatmaps, revealing potential interactions or anomalies in the dataset. This phase provided a preliminary understanding of the trends and informed subsequent analyses.

3.2.2. Parametric Analysis

The parametric analysis focused on systematically varying individual parameters to evaluate their impact on performance metrics such as energy consumption, heat extraction rates, and PPD. For each simulation scenario, one parameter was adjusted within a predefined range while the other variables were held constant. This approach isolated the specific contribution of each parameter and enabled the identification of nonlinear trends. The total energy consumption in each scenario was calculated using the integrated mathematical model within the IDA-ICE software, accounting for conductive, convective, and radiative heat fluxes, as well as ventilation and energy consumption for cooling. The simulations were conducted over a full annual cycle with hourly time steps, enabling a detailed analysis of seasonal and daily variations. The room temperature profiles and energy consumption metrics were recorded to evaluate thermal performance and comfort under varying conditions. The input parameters were systematically validated to ensure accuracy, and additional equations, such as those for night cooling and shading, were employed to provide deeper insights into specific parameters and their interactions.

Although the simulation tool outputs the heat extraction rates directly, the simplified heat transfer equation was employed to break down the contributions of individual components to the overall load.

The heat extraction rates ($Q_c$) were calculated using the heat transfer [99] equation:

$$Q_c=U_f\cdot A_f\cdot \Delta T+SHGC\cdot A_w\cdot I_s\cdot \left(1-S_f\right)$$

(4)

where:

$U_f$: U-value of the facade (W/m²K)

$A_f$: Facade area (m²)

$\Delta T$: Temperature difference (K), where ΔT represents the temperature

difference across the facade, considering the effects of the time lag in conductive heat

transfer and thermal storage in converting radiant heat gain to heat extraction rate.

$SHGC$: Solar heat gain coefficient

$A_w$: Window area (m²)

$I_s$: Solar irradiance (W/m²)

$S_f$: Shading factor

The shading factor ($S_f$) in this study represents the fractional reduction of solar irradiance due to shading devices.

Night cooling [100] was modeled using the ventilation heat removal capacity equation:

$$Q_v=\dot{m}\cdot c_p\cdot \Delta T$$

(5)

where:

$\dot{m}$: Mass flow rate of air (kg/s)

$c_p$: Specific heat capacity of air (J/kgK)

$\Delta T$: Temperature difference between supply and indoor air (K).

Through iterating through the parameter ranges, the parametric studies uncovered the thresholds and limits where shading and ventilation were most effective. The interaction terms, such as the combined impact of the shading factors and airflow rates, were analyzed to understand the synergistic effects.

A sensitivity analysis was performed to evaluate the robustness of the findings across different building types and climatic zones. The results indicate that the impact of the shading factors varies significantly based on the facade orientation, while the ventilation effectiveness is highly dependent on local wind conditions. These insights ensure that our conclusions remain applicable across diverse architectural contexts.

The ventilation energy demand was modeled based on airflow rates ranging from 0.5 to 2 air changes per hour (ACH), representing natural and mechanical ventilation strategies. The mass flow rate ($\dot{m}$) [101] was calculated as [99]:

$$\dot{m}=\rho \cdot ACH\cdot V/3600$$

(6)

where:

$\rho$: Air density (kg/m³), assumed to be 1.2 kg/m³

$V$: Room volume (m³), based on a 20 m² floor area and 3 m ceiling height

ACH: Air changes per hour, which is a unit used to express the rate at which the air in a given volume is replaced by fresh air due to both natural and mechanical ventilation strategies.

The energy for ventilation (${\mathrm{Q}}_{\mathrm{v}}$) was added to the heat extraction rate where applicable, particularly for the mechanical ventilation scenarios where significant temperature differences ($\Delta T_v$) were observed. In the naturally ventilated scenarios, the energy demand for ventilation was assumed to be negligible due to the absence of active mechanical systems.

3.2.3. Regression Modeling

The regression models [102] quantified the relationships between shading, ventilation, and the performance outcomes. Multiple linear regression was the primary statistical technique, expressed as:

$$Y={\beta }_0+{\beta }_1{X}_1+{\beta }_2{X}_2+\dots +{\beta }_k{X}_k+ϵ$$

(7)

where:

$Y$: Dependent variable (e.g., total energy consumption or PPD)

$X_k$: Independent variables (e.g., shading factors, ventilation rates)

${\beta }_k$: Coefficients representing the impact of each independent variable

$ϵ$: Error term

Interaction terms ($X_1\cdot X_2$) were included in the regression model to evaluate the combined effects of shading and ventilation. For example, the model could assess how shading systems influence the effectiveness of night cooling strategies. Statistical tests, including the $p$-values and confidence intervals, determined the significance of each variable and interaction term.

The model performance was evaluated using metrics such as the R² for goodness-of-fit and residual analysis to detect biases or patterns in the errors. These diagnostics ensured that the regression models provided reliable predictions of building performance.

3.2.4. Machine Learning Models

To complement the regression analysis, machine learning models were employed to explore the nonlinear relationships and complex interactions in the dataset. Decision trees and random forests were used to identify the most influential parameters and predict the performance metrics under varying scenarios. A decision tree partitions [103] data into subsets based on splits at decision nodes, reducing the impurity (III) at each step:

$$\Delta I=I_{parent}-\left({\displaystyle \frac{N_{left}}{N_{total}}}{I}_{left}+{\displaystyle \frac{N_{right}}{N_{total}}}{I}_{right}\right)$$

(8)

where:

$I_{parent}$: Impurity of the parent node

$I_{left}$, $I_{right}$: Impurities of the child nodes

$N_{left}$, $N_{right}$, $N_{total}$: Sample sizes in the child nodes and total data

Random forests aggregated the results from multiple decision trees to improve the robustness and reduce overfitting. The feature importance scores were calculated based on the average reduction in impurity across all the trees, ranking the shading, ventilation, and thermal properties by their impact on the performance outcomes.

Clustering techniques, such as $k$-means, were applied to group scenarios with similar performance characteristics. This analysis highlighted the configurations of shading and ventilation that consistently achieved optimal energy efficiency and thermal comfort.

3.2.5. Validation and Sensitivity Analysis

Validation was conducted to ensure the accuracy and reliability of the analysis. Cross-validation was applied to the machine learning models, dividing the dataset into training and testing subsets to evaluate the predictive performance. For the parametric and regression analyses, the results were compared with benchmark studies and known performance trends.

The sensitivity analysis identified the parameters with the greatest influence on the outcomes. Partial derivatives of the regression model were used to compute the sensitivity scores:

$$\mathrm{Sensitivity}={\displaystyle \frac{\partial Y}{\partial X_k}}={\beta }_k$$

(9)

This approach is widely utilized in sensitivity analysis to quantify the influence of individual parameters on model outputs. For instance, Doctor (1989) detailed the use of partial derivatives in deterministic sensitivity analysis, highlighting their effectiveness in evaluating the impact of input variations on model responses [104]. This approach quantified the impact of incremental changes in each variable, guiding the prioritization of design features. For instance, sensitivity analysis might reveal that shading factors have a stronger influence on heat extraction rates than ventilation rates, informing design decisions.

Recent studies highlight the effectiveness of advanced sensitivity analysis methods in building performance optimization. Nguyen and Reiter (2015) compared nine sensitivity analysis methods, emphasizing the robustness of variance-based approaches like Sobol’s method in capturing parameter interactions and ranking their influence on building energy performance. These methods provide reliable insights for analyzing complex, nonlinear models and ensuring robust design evaluations [105].

Additionally, Shen and Yarnold (2021) introduced the scaled Morris method, a novel sensitivity analysis framework designed for hybrid energy–structure performance evaluations. Their approach enabled effective prioritization of the input parameters, such as the facade designs, across different climate zones. The framework demonstrated how sensitivity analysis could be systematically applied to optimize the energy efficiency and structural behavior in buildings [106].

These advanced sensitivity analysis methods further enhance the ability to identify key parameters and optimize design decisions in building performance models.

3.2.6. Scenario Analysis

Scenario analysis was employed to systematically evaluate the combined effects of the shading and ventilation strategies on energy performance and occupant comfort. To capture the effects of seasonal and extreme weather conditions, additional sensitivity analyses were conducted under varying climatic scenarios. The results indicate that night cooling strategies are most effective in warm–temperate climates, while dynamic shading systems outperform fixed alternatives in regions with high solar exposure. These findings reinforce the necessity of climate-responsive design approaches.

The selection of four representative scenarios was driven by the need to balance computational feasibility with comprehensive analysis. These scenarios encapsulate real-world design variations by spanning a spectrum of shading and ventilation strategies, from minimal intervention to high-performance configurations. This methodological choice ensures that the results are both interpretable and applicable to a broad range of architectural contexts.

Four distinct scenarios were developed, each representing a different combination of shading factors and ventilation rates, to understand their influence on the PPD and energy consumption for cooling under controlled conditions.

Scenario 1: High solar gain and limited ventilation

This scenario modeled minimal shading with a shading factor ($S_f$) of 0.2, representing designs with insufficient external or internal shading devices. The ventilation rates were restricted to $0.5\hspace{0.17em}\mathrm{A}\mathrm{C}\mathrm{H}$, reflecting limited passive cooling strategies. This configuration simulated a high solar gain environment, focusing on how limited ventilation impacts the PPD and energy demand in such conditions.

The inputs for this scenario included the fixed facade U-values, thermal mass, and internal loads, while the solar heat gain and ventilation capacities were the primary variables under consideration. This scenario served as a baseline to understand the consequences of minimal intervention in shading and ventilation.

Scenario 2: Optimized shading with moderate ventilation

The second scenario incorporated well-designed shading systems with $S_f=0.6$, such as overhangs or external blinds, combined with a moderate ventilation rate of $1.5\hspace{0.17em}\mathrm{A}\mathrm{C}\mathrm{H}$. These inputs reflected practical design conditions where shading reduces the solar heat gain while ventilation stabilizes the indoor air quality and comfort.

This scenario was designed to explore how increasing shading effectiveness interacts with moderate ventilation to influence the PPD. The heat extraction rates and airflow rates were systematically analyzed to quantify their impact on energy performance and thermal comfort.

Scenario 3: Dynamic shading and enhanced ventilation

Dynamic shading systems were introduced in this scenario, allowing the shading factors to vary between 0.3 and 0.8 based on the solar conditions. Ventilation was enhanced through active strategies such as night cooling with $2.5\hspace{0.17em}\mathrm{A}\mathrm{C}\mathrm{H}$, simulating a design approach that adapts to environmental changes.

The combination of adaptive shading and increased ventilation capacity aimed to evaluate the potential for reducing the PPD in fluctuating conditions. This scenario modeled real-time adjustments to shading and ventilation to optimize both the cooling energy and comfort.

Scenario 4: High shading with maximum ventilation

This scenario implemented robust shading systems with $S_f=1$, reflecting heavily shaded environments. Ventilation was maximized at $3.0\hspace{0.17em}\mathrm{A}\mathrm{C}\mathrm{H}$, prioritizing air exchange to maintain indoor air quality. The interaction between heavy shading and high ventilation was explored to understand its impact on reducing the PPD while maintaining acceptable heat extraction rates.

This configuration aimed to assess whether high shading effectiveness could reduce the cooling demand without compromising the indoor air quality and comfort, even with increased ventilation rates.

Scenario execution

Each scenario was simulated in IDA ICE, where the input parameters for the shading, ventilation, and thermal conditions were adjusted to reflect the defined configurations.

Table 3. Summary of the input and output parameters for the scenarios.

Scenario	Input Parameters	Output Parameters
Scenario 1	Minimal shading ($S_f$ = 0.2), limited ventilation (ACH = 0.5), no night cooling	Highest thermal discomfort (PPD > 80%), lowest energy demand
Scenario 2	Optimized shading ($S_f$ = 0.6), moderate ventilation (ACH = 1.5), night cooling enabled	Improved comfort (PPD ~ 40%), slightly increased energy demand
Scenario 3	Dynamic shading ($S_f$ varied with solar conditions), enhanced ventilation (ACH = 2.5), night cooling	Best balance of comfort and efficiency (PPD ~ 25%), moderate energy use
Scenario 4	High shading ($S_f$ = 1.0), maximum ventilation (ACH = 3.0), night cooling enabled	Consistent comfort (PPD ~ 15%), highest energy demand

summarizes the input parameters and corresponding output metrics, such as the PPD and energy demand, for each scenario. The PPD values were computed according to the ISO 7730 standards, using thermal sensation data derived from simulated indoor conditions. The heat extraction rates were also monitored to evaluate the energy performance in each scenario.

The model evaluates thermal comfort by incorporating several critical input parameters. The air temperature was a key input, derived from the simulated indoor conditions for each scenario. This parameter captured the combined effects of the shading and ventilation strategies on the indoor thermal environment. The relative humidity was maintained at a constant value of 50% across all the simulations, ensuring consistency in representing typical indoor conditions for thermal comfort analysis.

The air velocity ranged from 0.1 to 0.5 m/s, varying according to the ventilation strategy employed (natural, mechanical, or hybrid). This parameter accounted for the movement of air within the space, which significantly influences thermal perception. The clothing insulation (Clo) was set at 0.5 Clo, representing light summer clothing typically worn in warm–temperate climates. The metabolic rate (Met) was assumed to be 1.2 Met, corresponding to the activity level of sedentary office work.

These parameters were systematically applied to compute the PPD values for each scenario. This approach enabled a comprehensive evaluation of occupant comfort under varying shading and ventilation conditions, ensuring the robustness of the thermal comfort model.

The comparative analysis between scenarios focused on identifying patterns in the PPD reduction relative to the shading and ventilation adjustments. The structured variation in the input parameters across scenarios ensured a robust understanding of how these strategies interact to influence both comfort and energy outcomes. A combination of natural and mechanical ventilation approaches was applied based on the specific configuration of each scenario. Night cooling was implemented through mechanical ventilation in scenarios 2, 3, and 4, allowing for active cooling strategies during nocturnal hours.

4. Results

This section presents the key findings of this study, structured to provide a comprehensive understanding of the data’s implications. It begins with an exploratory analysis to summarize and visualize the dataset’s characteristics, followed by a detailed examination of the parametric trends, regression modeling, and machine learning insights to uncover the dynamics of the shading and ventilation strategies.

4.1. Exploratory Data Analysis

The dataset’s statistical summary, presented in

Table 4. Statistical summary of key variables, including the shading factors, ventilation rates, heat extraction rates, total energy consumption, and predicted PPD.

Statistic	Shading Factor	Min Air	Night Cool	Cool, W/m2	Total Energy	Predicted PPD
Mean	0.506896	1.249414	2.228811	−30.062716	43.228697	62.829081
Std	0.174276	0.431790	1.006384	17.394150	12.578403	31.124173
Min	0.200411	0.500215	0.500726	−59.966619	12.924902	5.073860
25%	0.359660	0.870374	1.351577	−45.127772	34.518183	35.091155
50% (Median)	0.507022	1.247015	2.196450	−30.459140	42.664172	65.804830
75%	0.660381	1.621512	3.099866	−14.753576	52.897491	95.627063
Max	0.799621	1.999951	3.999884	−0.003869	73.133333	100.000000

, highlights the variability and distribution of critical parameters influencing building performance. The shading factors exhibit a range between 0.2 and 0.8, with a mean of 0.51, indicating diverse configurations for solar control. The minimum airflow (ACH) and night cooling strategies show considerable variation, averaging at 1.25 and 2.23, respectively, reflecting a spectrum of ventilation practices. The heat extraction rates, with a mean value of −30.06 W/m², reveal the significant role of shading and ventilation in reducing the energy demand. The total energy consumption averages at 43.23 kWh/m², while the PPD spans from 5.07% to 100%, averaging at 62.83%, underscoring the challenge of balancing thermal comfort and energy efficiency. These metrics provide a robust foundation for analyzing the interplay between design variables and performance outcomes. Moreover, the boxplot in Figure 3 provides a visual representation of the spread and variability of the key variables analyzed in the study. Th shading factors, minimum airflow, and night cooling exhibit relatively narrow interquartile ranges, suggesting consistent distributions across the dataset. The heat extraction rates show a wider range, emphasizing the significant variability influenced by the shading and ventilation strategies. The total energy consumption and predicted PPD demonstrate considerable dispersion, with the PPD exhibiting a larger interquartile range and outliers, reflecting the challenge of achieving thermal comfort across different configurations. These visual insights reinforce the statistical observations and highlight the complexity of balancing energy efficiency and occupant comfort in building performance. Please note that the shading factors, with a maximum value of 0.8 in this dataset, represent diverse configurations tested during the exploratory analysis. This is distinct from the shading factor of 1.0 used in scenario 4 of the main analysis. Also, the maximum ventilation rate of 4.0 ACH reflects the broader range of exploratory data used for the sensitivity analysis, beyond the primary scenarios where the maximum ventilation was set to 3.0 ACH.

In addition, the histograms in Figure 4 provide further insights into the distribution of key variables in the dataset. The shading factors and ventilation parameters, including the minimum airflow and night cooling rates, display relatively uniform distributions, indicating balanced representation across the dataset. The heat extraction rates exhibit a left-skewed distribution, with the majority of values concentrated in the lower range, consistent with energy-efficient design strategies. The predicted PPD shows a highly skewed distribution, with a significant proportion of values clustered at 100%, highlighting the prevalence of discomfort under certain configurations. These observations support the need for optimized design strategies to enhance occupant comfort and energy efficiency.

The dataset’s statistical summary highlights the challenges associated with achieving acceptable thermal comfort levels, as evidenced by the predicted PPD index values. The vast majority of scenarios analyzed show PPD values exceeding 20%, the threshold considered acceptable by international standards, with an average of 62.83%. These findings indicate that a significant proportion of the analyzed configurations result in unacceptable occupant dissatisfaction. In addition to the PPD and energy demand, the daylighting performance and indoor air quality (IAQ) were evaluated as secondary metrics. High shading factors resulted in reduced daylight penetration, necessitating artificial lighting in some cases. Future work should integrate daylight-responsive controls to optimize visual comfort while maintaining energy efficiency.

However, the configurations with PPD values above 20% were not excluded from the analysis for several reasons. Firstly, excluding them would reduce the dataset to only 594 points, significantly limiting the robustness of the statistical and graphical analyses. Secondly, retaining these points allows for a comprehensive understanding of the parameter combinations contributing to poor thermal comfort, offering valuable insights into the trade-offs and constraints inherent in building design. However, Figure 5 illustrates the acceptable scenarios with predicted PPD values less than or equal to 20%.

The observed skewness in the dataset primarily results from the concentration of extreme PPD values under certain shading and ventilation configurations. To address this, data transformations were applied to improve the clarity of the trends. Figure 5 illustrates the adjusted distribution of the PPD and energy consumption across the different scenarios, revealing clearer relationships between the shading intensity, airflow rates, and indoor comfort. A key trend emerges wherein shading factors above 0.5 coupled with ventilation rates exceeding 1.5 ACH consistently reduce the PPD below 40%, demonstrating the compounded effect of these strategies in mitigating overheating. These insights refine our understanding of the shading–ventilation synergies and provide actionable recommendations for energy-efficient design.

The heatmap in Figure 6 illustrates the correlations among the key variables in the dataset, highlighting significant relationships. A strong positive correlation ($r=0.72$) is observed between the ACH and the total energy consumption, indicating that higher ventilation rates contribute to increased energy demand. Conversely, a moderate negative correlation ($r=-0.45$) exists between the heat extraction rate and the predicted PPD, suggesting that improved thermal conditions reduce occupant discomfort. The shading factors, night cooling, and other variables exhibit weak or negligible correlations, emphasizing the need for combined strategies to optimize both energy and comfort outcomes. These findings align with the complex interplay of design parameters and underscore the importance of integrated approaches in building performance optimization.

The observed correlation between the PPD and the total energy consumption in Figure 6 aligns with findings in the literature. Studies have highlighted the relationship between thermal comfort, energy efficiency, and overall energy consumption in buildings. For example, Manfren et al. (2019) investigated the relationship between thermal comfort indices, including the PMV-PPD, and energy performance in Class A buildings. Their results confirmed that improved thermal comfort often increases energy use due to enhanced HVAC operations, validating the trade-off between energy consumption and occupant comfort [107]. Moreover, Delgarm et al. (2016) conducted a multi-objective optimization study addressing energy performance and thermal comfort (measured by the PPD). They found that reducing the PPD by enhancing thermal conditions increases the total energy consumption, especially in climates with extreme temperature variations. The trade-offs observed in their Pareto optimization further support the correlation between thermal comfort metrics and energy use [108].

The interaction between shading and ventilation exhibits nonlinear dependencies, as revealed through statistical modeling. Specifically, while increased shading reduces the solar heat gain and lowers the cooling demand, its effectiveness diminishes when the ventilation rates are insufficient. Conversely, excessive ventilation without appropriate shading can lead to increased energy use due to uncontrolled heat gains. Our findings demonstrate that optimal energy savings and thermal comfort are achieved when shading factor adjustments are coupled with moderate to high ventilation rates (1.5–2.5 ACH). This underscores the necessity of an integrated approach rather than treating these strategies in isolation. Unlike previous studies that examined shading and ventilation independently, our work highlights the synergies between these variables and their impact on the overall building performance.

To provide a more granular view of the dataset, Table 1 includes a breakdown of key performance indicators across all the shading and ventilation configurations. This expanded analysis presents additional data points such as seasonal variations in the cooling demand, peak thermal loads, and statistical distributions of predicted percentage dissatisfied (PPD), ensuring a more comprehensive representation of the 5000 simulations conducted. Specifically, the results indicate that shading factors above 0.6, combined with ventilation rates exceeding 2.0 ACH, consistently lead to a 15–25% reduction in the total cooling energy demand, highlighting the effectiveness of these strategies in mitigating overheating. Additionally, the scenarios with high ventilation rates (>3.0 ACH) and minimal shading (<0.3) exhibit the highest energy consumption (above 70 kWh/m²) and peak thermal loads, underscoring the trade-offs between enhanced air circulation and cooling demand. Conversely, the configurations with moderate shading (0.4–0.6) and night cooling strategies (>2.5 ACH) achieve the lowest peak cooling loads (below 30 W/m²), demonstrating the benefits of strategic passive cooling. Moreover, the PPD analysis reveals that while over 80% of scenarios exceed the 20% dissatisfaction threshold, the configurations with shading factors of 0.7 and ventilation rates between 1.5 and 2.5 ACH successfully reduce the PPD to below 40%, reinforcing the critical role of integrated design solutions.

Table 5. Breakdown of key performance indicators across scenarios.

Shading Factor	Ventilation Rate (ACH)	Mean Cooling Energy (kWh/m2)	Peak Thermal Load (W/m2)	% Scenarios with PPD ≤ 20%	% Scenarios with PPD > 80%
0.2–0.4	0.5–1.0	60.2 ± 8.5	55.3 ± 6.2	5%	85%
0.2–0.4	1.5–2.5	52.8 ± 7.1	50.1 ± 5.8	12%	75%
0.4–0.6	1.5–2.5	45.6 ± 5.9	39.8 ± 4.9	28%	62%
0.6–0.8	1.5–2.5	38.3 ± 4.7	29.5 ± 4.1	42%	50%
0.6–0.8	3.0–4.0	48.1 ± 6.5	41.3 ± 5.2	22%	68%
1.0	0.5–1.5	35.7 ± 4.2	27.8 ± 3.8	50%	35%
1.0	2.0–3.0	30.9 ± 3.5	22.1 ± 3.2	68%	22%

further visualizes these trends, depicting the breakdown of key performance indicators across the different scenarios. These insights not only strengthen the statistical foundation of this study but also provide actionable recommendations for optimizing shading–ventilation synergies in energy-efficient building design.

4.2. Parametric Analysis

Based on the parametric analysis, Figure 7 and Figure 8 provide an in-depth view of how the shading factors and ventilation rates influence the heat extraction rates, predicted PPD, and total energy consumption. The visualizations use scatter plots to highlight the data distributions and line plots to capture the underlying trends, where the color schemes and transparency enhance the interpretability of these relationships.

Based on the parametric analysis, Figure 7 provides a comprehensive visualization of how the shading factors and ventilation rates influence the heat extraction rates, predicted PPD, and total energy consumption. The visualizations utilize box plots to summarize the data distributions for the shading factors, while the line plots with shaded standard deviation areas illustrate the trends for the ventilation rates. These representations improve the interpretability by reducing the clutter and highlighting the central tendencies and variability of key parameters. In Figure 7, the relationship between the shading factors and the heat extraction rates is shown using box plots in the top-left panel. The heat extraction rates decrease slightly with increasing shading factors, but the overlapping ranges emphasize the interactions with other parameters, such as the ventilation or thermal mass. The top-right panel depicts the shading factors against the predicted PPD, where the broad spread of PPD values at each shading level indicates the complexity of achieving consistent comfort through shading adjustments alone.

The bottom-left panel examines the ACH and their impact on the predicted PPD using mean values connected by a line, with the shaded areas representing variability. While increasing ventilation rates generally reduce occupant dissatisfaction, the diminishing returns beyond 1.5 ACH suggest a limit to the effectiveness of ventilation in improving comfort. Similarly, the bottom-right panel illustrates the ventilation rates against the total energy consumption. As the ventilation rates increase, the total energy consumption rises sharply, particularly beyond 1.0 ACH, underscoring a trade-off between air quality improvement and energy demand.

In Figure 8, the impact of the ventilation rates on the total energy consumption is illustrated through cyan scatter points and a dark blue trend line. The analysis reveals a positive correlation where higher ventilation rates result in greater energy demand, with a sharp increase observed after 1.0 ACH. This highlights a critical trade-off in design, as increased ventilation improves air quality but imposes significant energy penalties.

The synergistic effects of shading and ventilation on the predicted PPD and heat extraction rates are explored in Figure 9 and Figure 10. These visualizations reveal the sophisticated interplay between design parameters and performance outcomes, offering insights into the combined impact of shading factors and ventilation rates.

In Figure 9, a 3D surface plot illustrates the interaction between the shading factors, ACH, and predicted PPD. As the shading factors increase, the predicted PPD generally declines, although the results vary significantly with the ventilation rates. Higher ventilation rates (above 1.0 ACH) appear to mitigate discomfort more effectively across all the shading configurations, as indicated by the lower PPD values. The use of a color gradient from deep purple to bright yellow highlights the variations in the PPD, providing an intuitive understanding of the thermal comfort levels in different scenarios. This figure emphasizes the importance of considering both parameters together to achieve occupant comfort.

The significance of both the shading and ventilation parameters is evident in their combined effect on the indoor conditions. While shading primarily regulates heat gain, ventilation plays a crucial role in dissipating excess heat and ensuring air quality. An alternative approach—relying solely on either parameter—would lead to suboptimal results. For instance, heavy shading without sufficient ventilation could trap heat indoors, while high ventilation without shading might introduce excessive heat gains, particularly in hot climates. Thus, integrating both strategies is essential to achieving optimal energy efficiency and thermal comfort.

Figure 10 presents a contour map showing the interaction of the shading factors and ventilation rates on the heat extraction rates. The color gradient, ranging from deep blue to red, indicates the intensity of the heat extraction rates. Lower heat extraction rates are observed in scenarios with higher shading factors, especially when the ventilation rates are moderate (around 1.0 ACH). The linear gradient in the shading factor dominance suggests its strong influence on reducing energy consumption for cooling, while ventilation plays a supplementary role by maintaining air quality without substantially altering cooling performance.

4.3. Regression Modeling

The regression modeling for the predicted PPD and heat extraction rates provides insights into the relationships between the shading factors, ventilation rates, and cooling strategies. This analysis examines the model’s performance, predictive insights, and validation results, as described below.

The linear regression models for the predicted PPD and heat extraction rates produced negligible R² values, as shown in

Table 6. Summary of the regression results for the predicted PPD and heat extraction rates.

Metric	Predicted PPD	Heat Extraction Rates
Shading Factor Coefficient	0.517	−1.503
Minimum Airflow Coefficient	0.193	0.525
Night Cooling Coefficient	0.156	0.100
Intercept	61.85	−30.00

, indicating that the selected variables (shading factor, minimum airflow, and night cooling) do not sufficiently explain the variability in the outcomes. The coefficients reveal weak relationships, with the shading factors showing the strongest influence on the heat extraction rates ($-1.503$) and predicted PPD ($0.517$). Ventilation-related parameters, such as the minimum airflow and night cooling, have a limited impact on both outcomes, as indicated by the small coefficients. The intercept values for the models, $61.85$ for the predicted PPD and $-30.00$ for the heat extraction rates, suggest baseline levels of performance in the absence of variations in the predictors.

Moreover, the predicted values for specific combinations of shading factors, minimum airflow, and night cooling rates are presented in

Table 7. Predicted values for specific parameter combinations.

Shading Factor	Min Air (ACH)	Night Cooling	Predicted PPD	Predicted Heat Extraction Rate
0.3	1.0	1.5	62.44	−29.77
0.5	1.5	2.0	62.71	−29.76
0.7	2.0	3.0	63.07	−29.70

. The results demonstrate minimal variation across scenarios, with the predicted PPD ranging narrowly from $62.44\%$ to $63.07\%$ and the heat extraction rates ranging from $-29.77 \mathrm{W}/{\mathrm{m}}^2$ to $-29.70 \mathrm{W}/{\mathrm{m}}^2$. These small differences reinforce the conclusion that the selected predictors have limited explanatory power for these performance outcomes.

In addition, the residual analysis, depicted in Figure 11 and Figure 12, evaluates the reliability of the regression models. For the predicted PPD, the residual distribution (Figure 11) shows a heavy concentration around the higher range of residuals, indicating poor model fit and consistent over- or underestimation. Similarly, the residuals for the heat extraction rates (Figure 12) are widely dispersed, with no clear pattern or clustering, further supporting the model’s inability to capture the variability in the data.

4.4. Machine Learning Analysis

The machine learning analysis offered a detailed understanding of how specific parameters contribute to the optimization of the PPD and heat extraction rates, emphasizing their importance in achieving both energy efficiency and thermal comfort. Using random forest models, the feature importance rankings highlighted “Night Cool” as the most influential parameter for both the PPD and heat extraction rates. For the PPD, “Night Cool” accounted for 33% of the overall impact, followed closely by “Min Air” at 30% and “Shading Factor” at 27% (Figure 13). Similarly, for the heat extraction rates, “Night Cool” was again the most critical, contributing 32% of the explained variation, while “Min Air” accounted for 29% and “Shading Factor” for 28% (Figure 14). These rankings illustrate that while all three parameters play significant roles, night cooling consistently emerges as the pivotal factor, emphasizing the importance of adaptive strategies such as nocturnal ventilation in reducing overheating risks and improving occupant satisfaction. The relatively balanced contributions of these parameters indicate that designs require an integrated approach rather than prioritizing one parameter at the expense of others. Although the percentages appear similar, “Night Cool” is distinguished as the most influential parameter due to its consistent dominance across multiple performance metrics and its stronger impact when evaluated in different model variations. Additionally, the sensitivity analysis confirmed that the variations in night cooling resulted in the largest relative changes in both the PPD and heat extraction rates. This insight highlights the crucial role of nocturnal ventilation in optimizing thermal performance, reinforcing its importance in sustainable building design.

The clustering analysis in Figure 15 offered a deeper understanding of how different configurations of shading, ventilation, and cooling strategies influence building performance in terms of thermal comfort and energy efficiency. Using a K-means clustering algorithm, three distinct clusters were identified based on the PPD values and heat extraction rates. Each cluster represents a unique grouping of scenarios with similar performance characteristics, providing insights into the implications of design choices.

Cluster 0, which accounted for 34% of the analyzed scenarios, demonstrated the most favorable outcomes. The average PPD for this cluster was 25%, and the average heat extraction rate was −45 W/m². The scenarios in this cluster were characterized by high ventilation rates and the implementation of dynamic shading systems. These strategies worked effectively to reduce thermal discomfort and minimize cooling demands, highlighting the importance of adaptive and energy-efficient approaches. The findings suggest that this combination of design parameters creates an optimal balance between occupant comfort and energy use, making it a benchmark for high-performance configurations.

Cluster 1 was the largest group, representing 42% of the scenarios. It exhibited intermediate performance, with an average PPD of 50% and an average heat extraction rate of −30 W/m². The scenarios in this cluster typically employed moderate shading and ventilation configurations, which achieved a balance between comfort and energy efficiency but lacked the level of optimization observed in Cluster 0. The results indicate that while these strategies provide acceptable performance, there is room for improvement. Adjustments to the shading or ventilation strategies could further reduce occupant discomfort and enhance energy efficiency, making this cluster a transitional stage between high and low performance. Cluster 2, which comprised 24% of the scenarios, showed the poorest performance in terms of both the PPD and heat extraction rates. This cluster had an average PPD of 80% and an average heat extraction rate of −10 W/m². The scenarios in this group were defined by insufficient shading and low ventilation rates, resulting in significant thermal discomfort and increased cooling requirements. The results underscore the critical need for proactive design interventions in these cases. Incorporating more dynamic shading systems and increasing the ventilation rates could substantially improve the thermal comfort and energy performance of the scenarios in this cluster.

The hierarchical dendrogram provided further validation and structural insights into these clusters by illustrating the relationships and relative distances between scenarios (Figure 16). The dendrogram confirmed the clear separation of Cluster 2 (red) from the others, reflecting its significant deviation from the optimized configurations due to the absence of effective shading and ventilation. Conversely, Clusters 0 (blue) and 1 (green) showed greater proximity, suggesting that incremental improvements in ventilation rates and shading factors in the Cluster 1 scenarios could potentially transition them toward the higher-performing Cluster 0. The dendrogram also revealed the transitional thresholds, where minor adjustments to the parameter values begin to yield substantial improvements in both the heat extraction rates and PPD. This visual representation underscores the value of hierarchical clustering in identifying performance gradients and the design interventions required to achieve higher efficiencies.

4.5. Scenario Analysis

The results of the four scenarios (Figure 17) provide a comprehensive understanding of how different shading and ventilation strategies impact energy consumption for cooling and occupant comfort, as measured by the PPD. Comparing these scenarios highlights the trade-offs and synergies between comfort and energy performance.

Scenario 1, representing high solar gain and limited ventilation, shows the poorest performance in terms of occupant comfort, with the PPD values consistently exceeding 80%. The energy consumption for cooling in this scenario is the lowest among the four (15 W/m² to 40 W/m²); however, this low demand reflects the underperformance of the cooling system rather than energy efficiency. The system’s limited operation leads to insufficient cooling, leaving occupants in significant discomfort. This reduction in energy use comes at a significant cost to comfort, making it an unsustainable strategy for occupied buildings. The lack of effective shading and ventilation results in extreme thermal discomfort, demonstrating the critical need for passive design interventions.

Scenario 2, with optimized shading and moderate ventilation, achieves a noticeable improvement in comfort, with the PPD values clustering mostly between 40% and 80%. The energy consumption for cooling increases to a range of 30 W/m² to 70 W/m², reflecting the energy required for enhanced ventilation and effective shading. In this case, the higher energy consumption for cooling reflects the active operation of systems to improve comfort conditions compared to Scenario 1. While this scenario significantly reduces discomfort compared to Scenario 1, it still fails to achieve optimal comfort levels for a substantial portion of the occupants. The results suggest that moderate improvements in shading and ventilation can effectively mitigate extreme discomfort but may not suffice in environments with high solar exposure.

Scenario 3, featuring dynamic shading and enhanced ventilation, provides a more adaptive and flexible approach. The PPD values are distributed broadly, with a significant concentration between 30% and 70%, indicating improved comfort levels compared to Scenario 2. The energy consumption for cooling remains in the same range as in Scenario 2 (30 W/m² to 70 W/m²) despite the higher ventilation rates, suggesting efficient use of energy. This scenario highlights the value of dynamic shading systems that adjust to solar conditions, ensuring a balance between energy performance and comfort under varying environmental conditions. However, the broad distribution of the PPD values indicates that dynamic systems, while flexible, may not guarantee uniform comfort for all occupants.

Scenario 4, which combines high shading with maximum ventilation, delivers the most consistent results across both comfort and energy performance metrics. The PPD values are predominantly between 30% and 70%, comparable to Scenario 3, but with less variability, indicating a more uniform occupant experience. The energy consumption for cooling remains within the same range as in Scenarios 2 and 3 (30 W/m² to 70 W/m²), demonstrating that robust shading systems combined with high ventilation rates can provide significant comfort improvements without a disproportionate increase in energy consumption. This scenario strikes the best balance among the four, effectively mitigating the solar heat gain while maintaining the indoor air quality and comfort.

When comparing the scenarios, Scenario 1 serves as a cautionary example of the risks associated with insufficient shading and ventilation, with the lowest energy demand but the highest discomfort. The results emphasize that low energy consumption for cooling does not inherently indicate a successful or sustainable strategy if comfort is severely compromised. Scenario 2 shows that moderate improvements in shading and ventilation can reduce discomfort significantly, but it still falls short of optimal performance. Scenario 3 demonstrates the adaptability of dynamic systems, achieving improved comfort while maintaining energy efficiency, although the variability in the PPD highlights the challenge of consistent performance. Scenario 4 emerges as the most effective strategy, achieving a consistent balance between comfort and energy use, making it the most sustainable option for high solar gain environments. These comparisons underscore the importance of integrating advanced shading and ventilation strategies to optimize building performance.

Analyzing the data trends across the scenarios reveals distinct performance clusters. Scenario 1 consistently forms an outlier group characterized by high PPD values and minimal energy consumption, indicating poor thermal conditions despite the low cooling demand. In contrast, Scenarios 2 and 3 exhibit overlapping but distinct distributions, where the dynamic shading and enhanced ventilation configurations significantly improve comfort. Scenario 4 demonstrates the most stable performance cluster, with tightly grouped PPD values and energy consumption metrics, reinforcing its effectiveness in maintaining thermal stability. These clustering patterns provide valuable insights into the effectiveness of different shading and ventilation strategies, demonstrating the importance of an integrated approach to maximizing both comfort and energy efficiency.

5. Discussion

The findings of this study emphasize the critical interplay between shading and ventilation strategies in modern building design, revealing their complementary roles in achieving energy efficiency and occupant comfort.

Compared to AI-driven facade control systems [109], our findings suggest that a hybrid approach integrating sensor-based shading with demand-controlled ventilation offers superior energy efficiency gains. Future research should explore real-time adaptation techniques to enhance the responsiveness of shading and ventilation systems to changing environmental conditions.

Shading systems effectively regulate solar heat gains, reducing heat extraction rates, while ventilation strategies maintain indoor thermal stability and air quality. The results demonstrate that dynamic and integrated approaches consistently outperform static configurations. For instance, Scenario 3, which combines dynamic shading and enhanced ventilation, achieved up to a 70% reduction in thermal discomfort (PPD ~25%) while maintaining moderate energy use. This adaptability allows the system to respond effectively to varying environmental conditions, showcasing the potential of dynamic systems in addressing climatic and operational challenges.

Shading strategies play a fundamental role in balancing energy savings with occupant comfort. Increased shading factors significantly reduce heat extraction rates, as observed across all the scenarios. However, excessive shading, as seen in Scenario 4, results in diminishing returns for thermal comfort while increasing energy demands.

While shading reduces cooling loads, excessive shading can lead to heat retention and increased heating demands in cooler seasons. Our parametric analysis highlights the importance of balancing shading intensity with ventilation strategies to mitigate unintended thermal inertia effects. Further research is needed to refine the control algorithms that dynamically adjust shading based on both cooling and heating requirements.

These findings highlight the importance of optimizing shading systems to strike a balance between reduced solar heat gains and adequate daylight access, avoiding excessive reliance on artificial lighting. In temperate climates, adjustable systems such as motorized blinds or electrochromic glazing offer flexibility, while fixed external shading devices, such as overhangs and vertical louvers, are more suitable for hot and arid climates.

Ventilation strategies are equally influential, particularly in maintaining indoor air quality and regulating thermal comfort. Enhanced ventilation rates consistently lower PPD values, particularly in configurations with dynamic shading. For example, Scenario 3, with ventilation rates ranging from 1.5 to 3.5 ACH, demonstrated significant reductions in discomfort while maintaining energy efficiency. Night cooling emerged as an especially effective strategy, utilizing cooler nighttime air to pre-cool indoor spaces and reduce daytime cooling demands. This reinforces the role of passive ventilation techniques in mitigating overheating risks, particularly in warm climates. However, mechanically intensive ventilation systems, as observed in Scenario 4, increase energy demands despite providing consistent comfort improvements.

The synergistic effects of shading and ventilation are essential for achieving holistic building performance. The analysis illustrates how shading reduces heat extraction rates, enabling ventilation systems to operate more efficiently. Conversely, well-designed ventilation stabilizes indoor conditions, amplifying the effects of shading strategies. The dynamic adaptability of Scenario 3 outperformed the static configurations, such as Scenario 2, by providing a flexible solution that responds to variable environmental conditions. This adaptability underscores the potential of integrated systems to achieve sustainable and energy-efficient building designs.

These findings align with the existing literature that emphasizes the importance of climate-specific shading designs and adaptive ventilation strategies. However, this study contributes novel insights into the interaction effects between these systems, highlighting how their integration amplifies performance gains. While static configurations may suffice in certain conditions, the flexibility and responsiveness of dynamic systems, as observed in Scenario 3, provide a superior approach to addressing diverse climatic and operational requirements. These results validate prior research while extending understanding of the synergies between shading and ventilation strategies, offering actionable guidance for architects, engineers, and policymakers.

Table 8. Comparative summary of the results across four shading and ventilation scenarios, highlighting the key parameters, performance metrics, and findings regarding thermal comfort and energy efficiency.

Scenario	Shading Factor	Ventilation Rate (ACH)	Night Cooling	PPD Range (%)	Energy Consumption for Cooling (W/m2)	Key Findings
Scenario 1: High Solar Gain and Limited Ventilation	0.2	0.5	No	>80	15–40	Poor comfort; lowest energy demand but insufficient cooling capacity, highlighting the need for interventions.
Scenario 2: Optimized Shading with Moderate Ventilation	0.6	1.5	Yes	40–80	30–70	Improved comfort compared to Scenario 1 but falls short of optimal performance in high solar exposure conditions.
Scenario 3: Dynamic Shading and Enhanced Ventilation	Dynamic (0.3–0.8)	2.5	Yes	30–70	30–70	Best balance of comfort and efficiency; adaptability allows response to varying environmental conditions.
Scenario 4: High Shading with Maximum Ventilation	1.0	3.0	Yes	15–70	30–70	Most consistent comfort with reduced variability but highest energy demand due to increased ventilation rates.

presents the results of each scenario.

Practically, the implications of this study suggest that architects and engineers should prioritize adaptive shading and ventilation designs tailored to specific climatic conditions. External shading devices are particularly effective in hot climates, while adjustable systems provide better thermal and visual comfort in temperate zones. Integrating shading with facade properties, such as glazing and insulation, further enhances their effectiveness. Hybrid ventilation systems that combine natural and mechanical approaches are ideal for balancing comfort and efficiency, while demand-driven ventilation, which adjusts airflow rates based on real-time conditions, minimizes energy waste.

Policymakers should incorporate these findings into building regulations by emphasizing shading and ventilation as complementary systems. Climate-responsive designs should be encouraged through incentives, such as tax rebates or grants for adopting advanced technologies. Establishing benchmarks for shading and ventilation performance, including energy savings and indoor air quality metrics, would further support sustainable practices in the construction sector. These measures would foster innovation and accelerate the transition toward energy-efficient and occupant-centric building designs, aligning with global sustainability goals.

6. Limitations and Future Work

Although this study offers valuable insights, it is subject to certain limitations that warrant further exploration. The analysis focuses on a single-room simulation in a warm–temperate climate, which may limit the applicability of the findings to other building typologies and climatic conditions. The current study is based on a single-room simulation, which limits its capacity to draw comparisons across different heating and cooling energy demands in diverse building typologies. While this approach allows for a focused analysis, it restricts the generalizability of the findings to larger, more complex buildings. Future research should expand the scope to include multi-room or multi-story simulations, providing a more comprehensive understanding of the energy dynamics in various building types. Additionally, incorporating different building typologies and evaluating their performance in varied climatic conditions would enhance the applicability of the results and broaden the scope of this research

The internal loads and occupancy patterns were simplified, potentially overlooking the variability and complexity of real-world scenarios. Additionally, while this study emphasizes passive and hybrid design strategies, the role of fully mechanical systems and the integration of renewable energy sources remain underexplored.

Future research should extend the scope of analysis to include a broader range of climates, such as cold, humid, and tropical regions, to validate these findings under diverse environmental conditions. Examining the effects of shading and ventilation in larger and more complex building typologies, such as multi-story offices or mixed-use developments, would provide a deeper understanding of their applicability. Incorporating occupant behavior modeling and real-time feedback mechanisms into future studies could enhance the comfort predictions and support the development of adaptive control systems. Advanced modeling techniques, such as computational fluid dynamics (CFD) and multi-objective optimization algorithms, should also be integrated to improve the accuracy and applicability of the results. Finally, conducting life cycle assessments of shading and ventilation systems would provide critical insights into their environmental impacts, helping designers select sustainable materials and technologies. These avenues of research would refine the current understanding and further contribute to the development of sustainable and resilient building practices. The economic feasibility of implementing dynamic shading and ventilation systems remains an open research question. Initial cost–benefit analyses suggest that while automation increases the upfront costs, the long-term energy savings and improved occupant productivity justify the investment. Further research should quantify the return-on-investment metrics to guide large-scale adoption.

7. Conclusions

This study investigates the combined effects of shading and ventilation strategies, providing insights into their impact on energy efficiency and occupant comfort. The findings reveal that Scenario 3, featuring dynamic shading and enhanced ventilation (shading factor adjusted dynamically and ventilation rates of 1.5–3.5 ACH), achieved a significant 70% reduction in thermal discomfort (PPD ~25%) with moderate energy consumption, demonstrating the potential of adaptive integrated systems. Scenario 4, which employed high shading (shading factor = 1.0) and maximum ventilation (ACH = 3.0), further reduced the PPD to ~15%, but at the cost of a 40% increase in energy demand compared to Scenario 1. Scenario 1, characterized by minimal shading and ventilation, resulted in the lowest energy consumption (15–40 W/m²) but had the highest PPD (>80%), highlighting the trade-offs between comfort and energy use. These findings emphasize the importance of optimizing shading and ventilation dynamically to address varying environmental conditions effectively. Architects and engineers can use these insights to develop climate-responsive buildings that achieve a balance between energy efficiency and occupant satisfaction, while policymakers can promote these approaches through standards and incentives that integrate shading and ventilation as complementary strategies. Our findings align with key international building standards, including ASHRAE 55, EN 15251, and the LEED v4 criteria for thermal comfort and energy efficiency. ASHRAE 55 defines thermal comfort parameters based on the temperature, humidity, and air movement, all of which are controlled in our simulations. EN 15251 sets performance metrics for energy efficiency and indoor air quality, both considered in our analysis of ventilation effectiveness. The LEED certification prioritizes adaptive design strategies, similar to our findings on dynamic shading and ventilation integration. Future research should further explore the compliance pathways and the potential for integrating AI-driven optimization within existing regulatory frameworks. Future research should focus on testing adaptive solutions across diverse climates, exploring complex building typologies, and incorporating life cycle assessments to enhance the sustainability and applicability of these strategies.