Optimising Window-to-Wall Ratio for Enhanced Energy Efficiency and Building Intelligence in Hot Summer Mediterranean Climates

Abstract

This study focused on optimising the window-to-wall ratio (WWR) as a design solution to reduce energy consumption and enhance building intelligence from an energy-saving perspective. It examines the impact range of the WWR in improving the energy efficiency in low-rise residential apartments in Sulaimaniyah City, which experiences a hot summer Mediterranean climate. This study employed a quantitative approach, simulating and analysing the energy consumption of the selected samples using specific tools, such as Autodesk Revit and Insight Cloud. The findings show that improving the window-to-wall ratio can significantly reduce the energy use intensity (EUI) across various building orientations. Southern-facing walls permit reductions ranging from 1.23 to 14.98 kWh/m², whereas northern-facing walls show losses ranging from 2.03 to 12.98 kWh/m². Similarly, western-facing walls show decreases ranging from 0.41 to 6.41 kWh/m² and eastern-facing walls from 1.44 to 5.59 kWh/m². These energy-saving ranges improve building intelligence in terms of energy utilisation. Furthermore, the recommended WWR is 65% for southern and eastern walls and 95% and 30% for northern and western walls. This study underscores the significance of optimising the window-to-wall ratio in intelligent building design. Neglecting this can significantly impact energy use and represents a missed opportunity to improve building intelligence.

1. Introduction

The future of the construction industry is dependent on intelligent building design. Sustainable design techniques and energy-saving strategies are essential to this field’s development [1]. An intelligent building is environmentally friendly since it uses less energy and water and creates fewer waste materials [2]. The Intelligent Building Institute (IBI) in the United States and the European Intelligent Building Group (EIBG) in the United Kingdom have established a standard definition of intelligent buildings. According to them, an intelligent building creates a productive and cost-effective environment by optimising four essential elements—structures, systems, services, and management—as well as their interconnections [3]. Intelligent buildings seamlessly combine active intelligence, which allows self-adjustment to environmental changes for comfort with little energy consumption, with passive intelligence, which employs architectural methods to satisfy climatic and contextual needs. This integration maximises occupant comfort while enhancing the energy efficiency. Moreover, intelligent design precedents can be found in well-constructed passive and low-energy buildings [4]. Furthermore, the subject of nearly zero-energy buildings (NZEBs) has garnered significant attention in the context of this framework in recent years [5,6]. As a result, by the end of 2020, all new construction was required to consist of “nearly zero-energy buildings”, according to the recast of the EU Directive on the Energy Performance of Buildings (EPBD) [7].

This study focuses on augmenting building intelligence by implementing passive strategies to mitigate energy consumption. Specifically, it explores the efficacy of adjusting the WWR to achieve this objective.

Windows play a crucial role in the energy consumption and visual comfort of buildings. Determining their areas, features, and proportions is one of the fundamental early design stage decisions, and it is difficult to change later [8].

The window-to-wall ratio is the total window or glazing area, including the mullions and frames, to the gross exterior wall area [9]. Adjusting the WWR significantly affects the energy efficiency, frequently outweighing the effects of changing the thickness of the external walls [10]. The area of a window is determined by the designers at the early stages of architectural design; thus, it is difficult or impossible to adjust afterwards. It is clear that windows significantly impact a building’s energy consumption [11]. An appropriate WWR value should be carefully selected for a façade at the early design stage [12]. Moreover, the WWR causes a linear change in the annual energy consumption for heating and cooling [13]. Alwetaishi’s research highlights the critical importance of the WWR in hot climates, demonstrating its effect on energy consumption and thermal comfort. Specifically, lowering the WWR to 20% resulted in a significant improvement, with the energy usage reduced by 15%. Notably, this reduction had a disproportionately large effect on the cooling demands rather than the heating requirements [14].

Pino et al. confirmed that a 100% WWR will generate a yearly total cooling and heating demand of 155 kWh/m² in Chile, whereas a 20% WWR may reduce the demand to 25 kWh/m² [15].

Alghoul et al. (2017) examined the impact of the WWR and window orientation on cooling, heating, and overall energy usage using the Energy Plus software (8.3.4). The researchers conducted an analysis of the thermal efficiency of a compact office building situated in Tripoli, Libya. They examined the impact of varying the WWR between 0 and 0.9, as well as adjusting the structure’s orientation in increments of 45°. Their findings indicate that as the WWR increases, there is a corresponding increase in the yearly cooling energy demand and a decrease in the annual heating load. Increasing the WWR in the southern walls leads to a considerable rise in the cooling energy demand, while the heating load is reduced to zero as a result of passive solar heating [16].

Gasparella et al. (2011) conducted a study to examine how various types of windows affect the energy requirements of a well-insulated residential building between winter and summer. They used meteorological data from Paris, Milan, Nice, and Rome to determine the most significant factors. The study’s findings suggest that including extensive glazing enhances the performance during winter, particularly for buildings facing south. Additionally, during winter, the utilisation of windows with a high total solar energy transmittance value is advantageous. Conversely, during summer, the implementation of glazing with a high total solar energy transmittance value significantly amplifies the cooling load of the building [17].

Persson et al. (2006) carried out research to examine the impact of reducing the south-facing window size and increasing the north-facing window size on the energy consumption in low-energy houses, using climate data from Gothenburg, Sweden. They employed a dynamic simulation tool called DEROB-LTH. The study’s findings suggested that minimising the glazing area of south-facing windows to zero could reduce the cooling needs, but this approach negatively affected the visual comfort. Consequently, the study recommends having larger windows on the north side while keeping the WWR on the southern façade to a minimum [18].

Another study found that the best WWR varies with the climate and orientation, although most ideal values lie within a narrow range (0.30 < WWR < 0.45). Exceptions are limited to south-facing facades in climates with extreme cold or heat, where the WWR values may vary from this range. It was shown that selecting the least favourable WWR configuration could significantly increase the total energy usage, ranging from 5% to 25% compared to applying the ideal WWR [19,20].

In addition, another study in Tehran looked at the relationship between energy usage and the window-to-wall ratio in high-rise office buildings. It discovered a direct correlation between the WWR and annual energy consumption, with a 20% drop in the WWR resulting in a 17% decrease in energy usage in the base-case model; in addition, the study discovered variances in the influence of the WWR on energy usage across different sides of the building, prioritising the sides in the following order: south, east, west, and north [21].

Another study that discussed the influence of window parameters on the thermal performance of office rooms in different climatic zones in Turkey concluded that the WWR is the most influential factor in annual energy consumption. North-facing office units have the highest energy demand, while south-facing units have the lowest. The optimal WWR for west-, east-, and south-exposed office units is 40–60% during the heating season. However, increasing the WWR does not lead to significant energy savings in all locations [22].

Moreover, a study investigating the impact of the WWR on the energy consumption in residential buildings in China’s hot summer and cold winter zones revealed a direct relationship between the WWR and overall energy consumption. Specifically, when the WWR rose, so did the overall energy usage. This effect was especially noticeable when the windows were oriented to the east or west [23]. Moreover, increasing the WWR produces an increase in cooling energy consumption and a decrease in heating energy consumption [16]. Due to solar radiation, specific WWR modifications are required to achieve the maximum energy efficiency. Data imply that raising the WWR enhances the energy consumption per unit area in south-facing rooms, calling for a reduction in the WWR for north-facing rooms to minimise the energy demand [24].

Previous studies have shown that the window-to-wall ratio affects the energy consumption in various geographical and climatic contexts. Additionally, energy is a key element of intelligent building design. Nevertheless, additional research is needed to determine the exact correlation between the WWR and energy use, as well as the extent to which it enhances building intelligence. This requirement is especially important for low-rise apartment complexes located in a hot summer Mediterranean climate, where the direction in which the windows face must also be taken into consideration. As a result, the objective of this research is to explicate the relationship between the WWR and energy use, as well as to determine the optimal WWR and the range of its influence on energy use. This will eventually improve building intelligence, enhance sustainability, and reduce greenhouse gas emissions and global warming.

2. Materials and Methods

This study employs a quantitative approach, combining a case study involving five low-rise residential apartments in various areas of Sulaimaniyah City (Farmanbaran, Nmwnayee, White City, Gwlishar, and Kurd City). Sulaymaniyah City, located in the northern region of Iraq, was chosen for this case study.

Sulaymaniyah has a Mediterranean, hot summer climate (Classification: Csa) according to the Köppen–Geiger climate classification [25]. The temperature averages 17.2 °C. The warmest month of the year is August, with an average temperature of 31.4 °C. January has the lowest temperatures, with an average reading of 3.4 °C.

The study started with visits to the projects to collect information and data in order to understand the physical characteristics and properties of the selected samples.

The study focused on the following information to create a precise model of the selected samples during data collection: the building area and dimensions, the window area and properties, the wall and roof construction, the insulation materials, the applied heating and cooling systems, and the orientation angles.

The data collected from the site visit revealed a consistent pattern among the selected samples regarding their construction materials. The walls were uniformly built using 20 cm thick concrete blocks, internally finished with 2 cm gypsum plaster and externally with 2 cm cement plaster. The roofs were composed of 20 cm thick concrete ceilings. Notably, the windows were double-glazed PVC with a structure of 4 mm clear glass on the interior, 140 mm air space, and 4 mm clear glass on the exterior. The absence of insulation materials in the roofs, walls, and flooring across all samples is worth mentioning, as shown in Figure 1.

However, variations in the orientation angles, ceiling heights, and window areas were observed. Each sample consisted of four identical apartments on a single floor with mirrored elevations. Additionally, air-cooled split systems, air-source heat pumps, and electric heaters were consistently employed for heating and cooling.

In addition, the research used Autodesk Revit 2024, a specialised software program for the creation of models based on the existing situations and the original dimensions and orientations of all samples, as shown in

Table 1. Two-dimensional and three-dimensional views of the research samples.

Sample	1	2	3	4	5
Plan layout
Top view
North and south orientation
East and west orientation
3D view

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Autodesk Insight Cloud was applied for the energy analysis and to examine the different scenarios of all samples, according to the window ratios located on the southern, northern, western, and eastern walls, as well as to determine the impact of the WWR on the energy use intensity (EUI) and its ability to enhance building intelligence. This study used Autodesk Revit and Autodesk Insight due to the growing emphasis on sustainable design practices in the architectural field since the 1980s and the modern, practical approaches of building information modelling (BIM) systems [26].

BIM tools allow designers to efficiently explore different design options throughout a project’s life cycle, saving time and resources while promoting the development of energy-efficient buildings [27].

As a result, this research used Insight Cloud to construct multiple scenarios by systematically altering the window-to-wall ratio across all samples. This approach enabled us to analyse the range of its impact on the energy use intensity and determine the optimal ratio across various orientations and sample compositions. The results were then compared to the current ratios to assess the possible range that would enable reduced energy usage and improve the intelligence capabilities of the selected buildings.

All the created models from the selected samples were analysed using the Insight Cloud platform to meet the research objectives. Based on the project’s energy settings, the building’s energy usage was quantified as the energy use intensity (EUI) in kWh/m² per year. The EUI was computed by dividing the building’s total annual energy consumption by its gross floor area. Therefore, any reduction in EUI resulting from optimising the WWR is deemed to indicate an intelligent approach within this design context.

As the cloud platform can be used to analyse additional features beyond the scope of this study, such as window shades, construction materials, lighting efficiency, and HVAC systems, these aspects could be explored in future studies. Based on the specific case and geographical data, the cloud evaluates each factor individually and visually illustrates its impact on the EUI. However, this study solely investigates the window-to-wall ratio. The cloud seamlessly interfaces with international weather stations near the sample locations. It analyses the data and generates multiple scenarios by changing the WWR to different values: the current ratio, 0%, 15%, 30%, 40%, 50%, 65%, 80%, and 95%.

The analysis process was applied to all samples. Different scenarios were created by changing the WWR and all four main orientations, as shown in Figure 2.

The energy analysis results were thoroughly reviewed and compared to earlier research findings. The purpose of this comparative analysis was to establish the optimal WWR for each building orientation, as well as the extent to which they impacted the energy use and building intelligence, as shown in Figure 3.

3. Results

The findings indicate notable variance among the samples, ranging from 325 kWh/m² to 385 kWh/m², with an average of 451.8 kWh/m², as shown in

Table 2. The total energy use for all samples.

Sample	Total Energy Use kWh/m2/year
Sample 1 (Farmanbaran)	412
Sample 2 (Nmwnayee)	444
Sample 3 (White City)	462
Sample 4 (Gwlishar)	458
Sample 5 (Kurd city)	483
Average	451.8

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In addition, the analysis from Insight Cloud shows that the total energy use is close to the maximum range, according to ASHRAE 90.1, a standard that provides the minimum requirements for the energy-efficient design of most sites and buildings, as shown in Figure 4.

The results reveal variations in the existing WWR across the different orientations of the selected samples: 19% to 24% in the southern walls, 19% to 29% in the northern walls, 11% to 28% in the western walls, and 11% to 27% in the eastern walls. These ratios are larger in the southern and northern walls than in the western and eastern walls.

These WWR variations correspond to diverse impacts on the EUI across the orientations, ranging from +1.7 kWh/m² to −3.32 kWh/m² in the southern walls, −0.54 kWh/m² to −2.21 kWh/m² in the northern walls, +0.2 kWh/m² to 2.04 kWh/m² in the western walls, and −0.06 kWh/m² to −0.71 kWh/m² in the eastern walls. Furthermore, the findings illustrate that the EUI differs by −3.32 kWh/m² to +2.04 kWh/m² based on the existing WWR in all samples and existing situations. These results signify a maximum EUI difference of 5.36 kWh/m², representing the maximum impact range of the WWR on the EUI across the sampled buildings, as shown in

Table 3. The effect of the existing WWR on the EUI for all samples according to the main orientations.

Sample	Southern Walls		Northern Walls		Western Walls		Eastern Walls
	Existing WWR	EUI (kWh/m2)	Existing WWR	EUI (kWh/m2)	Existing WWR	EUI (kWh/m2)	Existing WWR	EUI (kWh/m2)
Sample 1	20	+1.7	20	−0.76	21	+0.46	20	+0.83
Sample 2	25	−3.32	25	−1.27	28	+0.91	27	−0.06
Sample 3	27	+1.01	24	−0.59	14	+2.04	14	−0.71
Sample 4	19	−0.42	19	−0.54	18	+0.2	19	−0.31
Sample 5	24	−2.79	29	−2.21	11	+0.74	11	−0.26

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In addition, various scenarios based on different wall orientations were tested to determine the range of the WWR’s impact on the EUI and the intelligent capabilities of the selected buildings.

3.1. Southern Walls

The results demonstrate that in Sample 1, the EUI increased when the WWR was set at 0%, 15%, 65%, 80%, and 95%, resulting in EUI values ranging from 0.56 kWh/m² to 7.58 kWh/m². Conversely, the EUI decreased with WWR ratios of 30%, 40%, and 50%, with values ranging between −0.21 kWh/m² and −1.23 kWh/m².

Similar trends were observed in the other samples, where the EUI decreased across all tested ratios except 0% and 15%. The decrease ranged from −14.98 kWh/m² to −0.21 kWh/m², while the increases ranged from +2.05 kWh/m² to +14.77 kWh/m², as shown in Figure 5.

Moreover, the maximum decrease in the EUI across all samples on the southern walls ranges from −1.23 to −14.98 kWh/m². Conversely, the maximum increase in the EUI ranges from 7.58 to 16.3 kWh/m², while the maximum difference in the EUI spans 8.81 to 24.52 kWh/m² in this orientation, as shown in

Table 4. The maximum variation in the EUI resulting from altering the WWR, specifically in southern walls.

Sample	Maximum Decrease in EUI kWh/m2	Maximum Increase in EUI kWh/m2	Maximum Difference in EUI kWh/m2
Sample 1	−1.23	+7.58	8.81
Sample 2	−14.98	+16.3	31.28
Sample 3	−10.87	+14.77	25.64
Sample 4	−10.78	+11.3	22.08
Sample 5	−12.33	+12.19	24.52
Average	−10.038	+12.428	22.466

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In addition, the research findings indicate that increasing the WWR in the southern walls by various percentages (30%, 40%, 50%, 65%, 80%, and 95%) leads to different levels of decrease in the EUI. The maximum decreases in the EUI are observed at (7.25 kWh/m²), (9.45 kWh/m²), (11.66 kWh/m²), (14.98 kWh/m²), (13.15 kWh/m²), and (11.32 kWh/m²), respectively. Conversely, at WWR ratios of 0% and 15%, the EUI increases by (16.3 kWh/m²) and (7.12 kWh/m²), respectively, as shown in

Table 5. The maximum change in the EUI when changing the WWR in southern walls.

WWR	0% kWh/m2	15% kWh/m2	30% kWh/m2	40% kWh/m2	50% kWh/m2	65% kWh/m2	80% kWh/m2	95% kWh/m2
Maximum decrease in EUI	0	0	7.25	9.45	11.66	14.98	13.15	11.32
Maximum increase in EUI	16.3	7.12	0	0	0	0	3.87	7.19
Maximum difference	16.3	7.12	7.25	9.45	11.66	14.98	17.02	18.51

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3.2. Northern Walls

In the northern walls, differences among the samples are evident. In Sample 1, the maximum decrease in EUI occurs at a WWR of 65%, reducing it by −4.43 kWh/m². In Samples 2 and 5, the maximum decrease in EUI is observed at a WWR of 95%, with reductions of −12.93 kWh/m² and −12.98 kWh/m², respectively. Sample 3’s maximum decrease is at a WWR of 65%, amounting to −3.6 kWh/m². In Sample 4, the maximum decline in EUI occurs at a WWR of 30%, with a reduction of −2.03 kWh/m², as illustrated in Figure 6.

Overall, the maximum decrease in the EUI across all samples in the northern walls ranges from −2.03 to −12.98 kWh/m². Conversely, the maximum increase in the EUI ranges from +1.73 to +3.69 kWh/m². The maximum difference in the EUI across all samples in this orientation spans 5.53 to 16.62 kWh/m², as shown in

Table 6. The maximum variation in the EUI resulting from altering the WWR, specifically in northern walls.

Sample	Maximum Decrease in EUI kWh/m2	Maximum Increase in EUI kWh/m2	Maximum Difference in EUI kWh/m2
Sample 1	−4.43	+1.73	6.16
Sample 2	−12.93	+3.69	16.62
Sample 3	−3.6	+2.45	6.05
Sample 4	−2.03	+3.5	5.53
Sample 5	−12.98	+3.14	16.12
Average	−7.194	+2.902	10.096

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The research findings also indicate that increasing the window-to-wall ratio (WWR) in the northern walls from 15% to 95% results in a decrease in the energy use intensity (EUI) ranging from 0.14 kWh/m² to 12.98 kWh/m². Conversely, at a WWR of 0%, the EUI increases. The maximum increase in the EUI ranges from 0.72 kWh/m² to 3.69 kWh/m². Furthermore, the maximum difference in the EUI for all WWR scenarios ranges from 0.86 kWh/m² to 16.48 kWh/m², as illustrated in

Table 7. The maximum change in the EUI when changing the WWR of the southern walls.

WWR	0% kWh/m2	15% kWh/m2	30% kWh/m2	40% kWh/m2	50% kWh/m2	65% kWh/m2	80% kWh/m2	95% kWh/m2
Maximum decrease in EUI	0	0.14	2.39	4.22	6.18	9.11	11.02	12.98
Maximum increase in EUI	3.69	0.72	0	0	0	0	1.61	3.5
Maximum difference	3.69	0.86	2.39	4.22	6.18	9.11	12.63	16.48

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3.3. Western Walls

The analysis indicates a general EUI increase across all samples as the WWR rises from 30% to 95%. However, there is variability in the extent of this change among the samples. For example, in Sample 1, the EUI change ranges from +0.16 to +6.31 kWh/m², while, in Sample 2, it ranges from +0.75 to +8.6 kWh/m². In Sample 3, the range spans −6.41 to +13.82 kWh/m², in Sample 4, −1.35 to +13.58 kWh/m², and in Sample 5, −0.41 to +18.28 kWh/m².

Additionally, the EUI range at a WWR of 15% is narrower than at 0% across all samples. Furthermore, computing the average EUI across all WWRs and samples highlights a sequence from low to high energy use, namely 0%, 30%, 40%, 50%, 65%, 80%, and 95%, as presented in Figure 7.

Moreover, the maximum decrease in EUI across all samples in the western walls ranges from −0.41 to −6.41 kWh/m². Conversely, the maximum increase in EUI across all samples in the western walls ranges from +6.31 to +18.28 kWh/m², with an average impact of 12.11 kWh/m². Additionally, the maximum difference in EUI across all samples in this orientation spans 6.31 to 20.23 kWh/m², as shown in

Table 8. The maximum variation in the EUI resulting from altering the WWR, specifically in western walls.

Sample	Maximum Decrease in EUI kWh/m2	Maximum Increase in EUI kWh/m2	Maximum Difference in EUI kWh/m2
Sample 1	0	6.31	6.31
Sample 2	0	8.6	8.6
Sample 3	−6.41	13.82	20.23
Sample 4	−1.35	13.58	14.93
Sample 5	−0.41	18.28	18.69
Average	−1.634	12.118	13.752

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The research findings also demonstrate that the EUI decreases by 2.32 to 4.32 kWh/m² when the WWR is set at 15%, 30%, and 40%. Conversely, at WWR ratios of 0%, 50%, 65%, 80%, and 95%, the EUI increases within the range of 3.06 to 18.28 kWh/m². Furthermore, the maximum difference in the EUI across all WWR scenarios falls between 3.06 and 18.28 kWh/m², as detailed in

Table 9. The maximum change in the EUI when changing the WWR in the western walls.

WWR	0% kWh/m2	15% kWh/m2	30% kWh/m2	40% kWh/m2	50% kWh/m2	65% kWh/m2	80% kWh/m2	95% kWh/m2
Maximum decrease in EUI	0	2.32	6.41	4.32	2.24	0	0	0
Maximum increase in EUI	3.06	1.91	0.75	2.01	4.42	8.04	13.16	18.28
Maximum distance	3.06	4.23	7.16	6.33	6.66	8.04	13.16	18.28

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3.4. Eastern Walls

The results indicate a fluctuating EUI with a changing WWR in the eastern walls. The EUI increases at 0% and 15% ratios; decreases at 30%, 40%, 50%, and 65%; and then increases again at 80% and 95% for most samples.

The average EUI across all WWR scenarios and samples is 3.02 kWh/m², observed at a 0% ratio, while the minimum EUI is −3.322 kWh/m² at a 65% ratio. Moreover, scenarios with WWRs of 15%, 80%, and 95% exhibit a lower EUI compared to 0% but a higher EUI than 30%, as shown in Figure 8.

Moreover, the maximum decrease in EUI across all samples in the eastern walls falls between −1.44 and −5.59 kWh/m².

Conversely, the maximum increase in EUI across all samples in the eastern walls ranges from +1.35 to +6.53 kWh/m², with an average impact of 3.08 kWh/m². Additionally, the maximum difference in EUI across all samples in this orientation ranges from +3.11 to +12.12 kWh/m², as detailed in

Table 10. The maximum variation in the EUI when altering the WWR, specifically in eastern walls.

Sample	Maximum Decrease in EUI kWh/m2	Maximum Increase in EUI kWh/m2	Maximum Difference in EUI kWh/m2
Sample 1	−5.59	6.53	12.12
Sample 2	−1.44	3.47	4.91
Sample 3	−3.92	2.1	6.02
Sample 4	−1.76	1.35	3.11
Sample 5	−4.08	1.95	6.03
Average	−3.358	3.08	6.438

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Additionally, the most significant reduction in the energy use intensity occurs within the range of 1.07 to 5.59 kWh/m² when the window-to-wall ratio is set at 30%, 40%, 50%, 65%, and 80%. Conversely, at WWR ratios of 0%, 15%, and 95%, the EUI generally increases across these ratios by 1.25 to 6.53 kWh/m². The maximum variation in the EUI across all WWR scenarios falls between 3.32 and 6.53 kWh/m², as outlined in

Table 11. The maximum change in the EUI when changing the WWR in the western walls.

WWR	0% kWh/m2	15% kWh/m2	30% kWh/m2	40% kWh/m2	50% kWh/m2	65% kWh/m2	80% kWh/m2	95% kWh/m2
Maximum decrease in EUI	0	1.07	4.08	4.03	4.06	5.59	4.21	2.83
Maximum increase in EUI	6.53	2.25	0	0	0	0	0	1.25
Maximum distance	6.53	3.32	4.08	4.03	4.06	5.59	4.21	4.08

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To comprehensively understand the extent of the impact resulting from changes in the window-to-wall ratio on the existing energy use intensity, this study observes that the maximum ranges are as follows: 2.14% to 7.05% in the southern walls, 1.21% to 3.74% in the northern walls, 1.53% to 4.38% in the western walls, and 0.68% to 2.94% in the eastern walls. These are shown in

Table 12. The range of the WWR’s impact on the current EUI of all samples.

Southern Walls
WWR	0%	15%	30%	40%	50%	65%	80%	95%	Maximum Difference (kWh/m2)	Existing EUI (kWh/m2)	Impact Range %
Sample 1	7.58	3.17	−1.23	−0.72	−0.21	0.56	3.87	7.19	8.81	412.00	2.14
Sample 2	16.30	4.53	−7.25	−9.45	−11.66	−14.98	−13.15	−11.32	31.28	444.00	7.05
Sample 3	14.77	7.12	−0.52	−3.11	−5.70	−9.58	−10.22	−10.87	25.64	462.00	5.55
Sample 4	11.30	2.05	−7.21	−8.23	−9.25	−10.78	−6.14	−1.49	22.08	458.00	4.82
Sample 5	12.19	2.83	−6.53	−8.19	−9.85	−12.33	−10.52	−8.70	24.52	483.00	5.08
Average											4.93
Northern Walls
WWR	0%	15%	30%	40%	50%	65%	80%	95%	Maximum Difference (kWh/m2)	Existing EUI (kWh/m2)	Impact Range %
Sample 1	1.73	−0.14	−2.01	−2.70	−3.39	−4.43	−4.08	−3.73	6.16	412.00	1.50
Sample 2	3.69	0.72	−2.26	−4.22	−6.18	−9.11	−11.02	−12.93	16.62	444.00	3.74
Sample 3	2.45	0.55	−1.35	−1.99	−2.64	−3.60	−3.56	−3.52	6.05	462.00	1.31
Sample 4	2.03	0.00	−2.03	−1.53	−1.02	−0.27	1.61	3.50	5.53	458.00	1.21
Sample 5	3.14	0.37	−2.39	−4.12	−5.85	−8.45	−10.71	−12.98	16.12	483.00	3.34
Average											2.22
Western Walls
WWR	0%	15%	30%	40%	50%	65%	80%	95%	Maximum Difference (kWh/m2)	Existing EUI (kWh/m2)	Impact Range %
Sample 1	1.18	0.67	0.16	1.03	1.90	3.21	4.76	6.31	6.31	412.00	1.53
Sample 2	3.06	1.91	0.75	1.59	2.43	3.69	6.14	8.60	8.60	444.00	1.94
Sample 3	1.77	−2.32	−6.41	−4.32	−2.24	0.88	7.35	13.82	20.23	462.00	4.38
Sample 4	2.53	0.59	−1.35	0.26	1.86	4.27	8.92	13.58	14.93	458.00	3.26
Sample 5	1.41	0.50	−0.41	2.01	4.42	8.04	13.16	18.28	18.69	483.00	3.87
Average											3.00
Eastern Walls
WWR	0%	15%	30%	40%	50%	65%	80%	95%	Maximum Difference (kWh/m2)	Existing EUI (kWh/m2)	Impact Range %
Sample 1	6.53	2.25	−2.02	−3.04	−4.06	−5.59	−4.21	−2.83	12.12	412.00	2.94
Sample 2	3.47	1.51	−0.45	−0.73	−1.02	−1.44	−0.23	0.97	4.91	444.00	1.11
Sample 3	2.10	−0.91	−3.92	−3.92	−3.92	−3.91	−1.78	0.35	6.02	462.00	1.30
Sample 4	1.35	0.04	−1.28	−1.41	−1.55	−1.76	−0.76	0.23	3.11	458.00	0.68
Sample 5	1.95	−1.07	−4.08	−4.03	−3.98	−3.91	−1.33	1.25	5.58	483.00	1.16
Average											1.44

.

Ultimately, this research highlights that upon implementing the optimal scenario across all samples, the existing EUI could decrease from −11.25 kWh/m² to −29.8 kWh/m². This reduction translates into an improvement of 2.73% to 6.61% for the selected samples, as shown in

Table 13. The changes in the existing EUI that result from applying the best scenario to all samples.

Sample	Existing EUI (kWh/m2)	Best WWR Scenario According to Wall Direction (kWh/m2)				Total (kWh/m2)	Total Impact of WWR on Existing EUI (%)
		Southern Walls	Northern Walls	Western Walls	Eastern Walls
Sample 1	412	−1.23	−4.43	0	−5.59	−11.25	2.73
Sample 2	444	−14.98	−12.93	0	−1.44	−29.35	6.61
Sample 3	462	−10.87	−3.6	−6.41	−3.92	−24.8	5.37
Sample 4	458	−10.78	−2.03	−1.35	−1.76	−15.92	3.48
Sample 5	483	−12.33	−12.98	−0.41	−4.08	−29.8	6.17

.

4. Discussion

This study found considerable variance in energy usage among the samples, ranging from 325 kWh/m² to 385 kWh/m², with an average of 451.8 kWh/m². This energy consumption level is far greater than that indicated for structures designed to be energy-efficient, such as those complying with passive housing standards, which require no more than 120 kWh/m² [28]. Additionally, the findings show that the overall energy use across all samples approaches the maximum limit given by ASHRAE 90.1. This result implies that energy efficiency was not fully considered during the design process, resulting in a lower level of intelligent design in these buildings.

Regarding the existing WWR and its impact on the EUI, the data show that the EUI was reduced in all directions for most of the samples, except in the western walls, where the EUI increased due to the original WWR. This finding suggests that the WWR in the western walls could have been better studied throughout the design phase compared to other orientations. Furthermore, the research findings reveal that the energy usage variation ranges from a decrease of −3.32 kWh/m² to an increase of +2.04 kWh/m² across all samples and orientations. This indicates that the maximum disparity in the EUI linked to the WWR among the samples is 5.36 kWh/m², representing the most extensive impact range of the WWR on the EUI within the existing dataset.

Concerning the scenarios implemented in the selected samples, they are as follows.

4.1. Southern Walls

The southern walls have a significant drop in energy use intensity, ranging from −1.23 to −14.98 kWh/m² across all samples. The most significant impact is 14.98 kWh/m², comparable to 3.31% of the average EUI of all samples. This finding highlights the possibility of adjusting the WWR in the southern walls to minimise the energy use while significantly improving the building intelligence. Furthermore, these findings corroborate prior research emphasising the importance of the WWR in southern walls, which have a more significant impact on the EUI than walls of other orientations [21]. However, some studies, particularly those focused on cold climates, such as Sweden, recommend reducing the WWR to zero on southern walls [18].

In addition, the data show that a 65% ratio significantly influences the reduction in energy usage within the analysed buildings compared to other WWR arrangements. This study implies that enhancing the WWR by around 65% in the southern façades of low-rise residential complexes in hot summer Mediterranean climates yields a significant reduction in energy consumption. This range is higher compared to that of other hot-climate countries [19,20]. This optimisation reduces the energy consumption while increasing the architectural intelligence in buildings, particularly regarding their energy consumption dynamics.

4.2. Northern Walls

In the context of the northern walls, this study found significant potential for a lower energy use intensity across all samples, ranging from −2.03 to −12.98 kWh/m². The most significant impact is 12.98 kWh/m², 2.87% of the average EUI across all samples. These findings indicate that optimising the WWR on the northern walls can result in an average reduction of 7.194 kWh/m² in energy consumption, increasing the building intelligence regarding energy utilisation.

Furthermore, investigating the influence of increasing the WWR in the northern walls from 15% to 95% reveals a proportional decrease in the EUI, ranging from −0.14 kWh/m² to −12.98 kWh/m². These findings contrast sharply with the scenarios including a 0% WWR in which the EUI grows. Notably, the most significant reduction in EUI is observed at a WWR of 95%, demonstrating the importance of optimising the WWR in optimising the energy performance and strengthening the building intelligence capabilities. Subsequently, this study emphasises the importance of raising the WWR in the northern walls by up to 95% as a significant aspect of improving building intelligence through energy optimisation. This result aligns with previous studies in the field, which recommend larger windows on north-facing walls [18]. Furthermore, this study emphasises the nuanced impact of the WWR on the EUI, noting a less substantial effect in the northern walls compared to the southern walls in low-rise residential apartments located in hot summer Mediterranean climates, with an average decrease of 2.84 kWh/m² among the selected samples.

4.3. Western Walls

This study reveals that increasing the window-to-wall ratio directly correlates with the energy use intensity in western walls, especially after exceeding the 30% threshold, highlighting the influential role of the window’s ratio.

Moreover, a detailed analysis of the fluctuations in the EUI across the spectrum of WWRs from 30% to 95% reveals a substantial range of variability, spanning −6.41 kwh/m² to +18.28 kwh/m². The highest impact observed is −6.41 kWh/m², which accounts for 1.41% of the average EUI of all samples. These findings are consistent with previous studies that indicate that the WWR has a weaker impact on energy use on western-facing walls compared to southern-facing walls [21].

Such significant fluctuations underscore the nuanced interplay between architectural design elements and energy performance metrics, highlighting the multifaceted nature of building energy dynamics.

In addition, the empirical analysis delineates the optimal WWR for the western walls, revealing that a ratio of 30% emerges as the most favourable configuration across the tested samples. Moreover, examining the average energy use intensity across all situations and samples reveals a distinct pattern, with the following sequence observed: 30%, 40%, 15%, 50%, 0%, 65%, 80%, and 95% of the WWR on the western walls.

4.4. Eastern Walls

The EUI examination across all scenarios and samples in the eastern walls reveals significant patterns in the WWR. Specifically, the average EUI across all situations is 3.02, equivalent to a WWR of 0%. The minimal EUI of −3.322 corresponds to a WWR of 65%, while the maximum impact of the WWR is −6.41 kWh/m², accounting for 1.41% of the average EUI of all samples. The further investigation of specific scenarios yields promising insights into the relative energy efficiency implications of different WWR designs. Scenarios with WWR values of 15%, 80%, and 95% show lower EUIs than the baseline scenario of 0% but greater EUIs than the optimal WWR of 30%.

Based on the cumulative analysis, the scenario with a WWR of 65% is identified as the optimum configuration in this direction.

In addition, this study analysed the maximum impact range of the WWR on the existing EUI across different walls to comprehensively understand the maximum difference in EUI across various scenarios and samples. WWR changes considerably impact the EUI of southern walls, with the maximum effects ranging from 2.14% to 7.05%. Western walls exhibit a significantly lower effect, ranging from 1.53% to 4.38%. Northern walls have a moderate impact, ranging from 1.21% to 3.74%. Eastern walls have the least pronounced influence, ranging from 0.68% to 2.94%.

A range of similarities can be observed with previous studies regarding the impact of the WWR on energy use based on the wall orientation, and most studies agree that this effect is more pronounced on southern walls [16,17,21].

Moreover, this study concludes that implementing the best scenario across all samples will significantly lower the existing energy use intensity, which ranges from 11.25 kWh/m² to 29.8 kWh/m².

These results represent a substantial decrease of 2.73% to 6.61% for the selected samples. Such findings underscore the potential of optimising the window-to-wall ratio in low-rise residential apartments in hot summer Mediterranean climate zones to significantly lower the annual energy consumption, thereby enhancing the building intelligence within these ranges.

5. Conclusions

Optimising the WWR is a design approach that can improve building intelligence in low-rise residential apartment buildings. Optimising the WWR reduces the energy usage intensity (EUI) within varying ranges: 1.23 to14.98 kWh/m² in southern walls, 2.03 to 12.98 kWh/m² in northern walls, 0.41 to 6.41 kWh/m² in western walls, and 1.44 to 5.59 kWh/m² in eastern walls. These decreases improve the building intelligence in terms of energy efficiency. In hot summer Mediterranean regions, the ideal WWR for low-rise apartment buildings is 65% for the southern and eastern walls, 95% for the northern walls, and 30% for the western walls. Applying the optimum WWR scenario in all directions and for all selected samples reduces the existing EUI from 2.73% to 6.61%, boosting the intelligence possibilities in this type of building. In addition, the southern walls are the most sensitive to WWR fluctuations, followed by the western, northern, and eastern walls. The optimisation of the WWR for energy efficiency is directly related to the building orientation, as each orientation necessitates a specific WWR range for optimal performance. As a result, building apartments with mirrored elevations in all directions is less suitable for the maximisation of the energy efficiency. Therefore, an energy analysis of residential apartments, particularly the WWR in the initial design stage, is essential. It improves the energy efficiency, enhances the building intelligence, and reduces the gas emissions and operating costs.