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28 June 2023

Evaluating Savings Potentials Using Energy Retrofitting Measures for a Residential Building in Jeddah, KSA

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and
1
Architectural Facades & Products Research Group, Department of Architectural Engineering + Technology, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, The Netherlands
2
Faculty of Environmental Designs, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
This article belongs to the Special Issue Thermal Comfort in Built Environment: Challenges and Research Trends

Abstract

Residential buildings in the Kingdom of Saudi Arabia (KSA) contribute to nearly half of the overall electricity consumption in the building stock, highlighting their significant role in energy consumption. While an upgraded energy code has been established and enforced for new buildings, existing buildings continue to operate at the same level of energy consumption. Therefore, there is a need for further energy upgrades in existing buildings. This study evaluates the energy savings potential of various energy retrofitting measures for a case study in Jeddah, KSA. Data from previous studies and current practices were collected and analyzed. Different energy upgrade measures, such as windows replacement, wall insulation upgrade, roof insulation upgrade, and air conditioning unit replacement, were selected and evaluated using a digital simulation tool called Design-Builder. The simulation results were compared to understand the potential percentage of energy savings. The average annual energy consumption (AAEC) was used as the primary performance indicator to compare the energy savings among the scenarios. The results demonstrate significant reductions in energy consumption for the proposed scenarios. Furthermore, the study examined the significant impact of uncertainties, specifically, the infiltration rate and AC setback temperature, on AAEC. In conclusion, the proposed scenarios have the potential to achieve substantial energy savings, ranging from 25% to 66%, depending on the number of energy retrofitting interventions employed. The findings of this study can serve as a useful reference for similar energy retrofitting projects.

1. Introduction

In the Kingdom of Saudi Arabia (KSA), building energy consumption is a significant contributor to oil consumption and a major expense for building users []. The government has introduced numerous initiatives to promote energy efficiency and renewable energy, recognizing the importance of energy efficiency in buildings []. While the Saudi building code (SBC) committee has endorsed the upgraded energy code and required it for new residential buildings in 2021, existing buildings continue to consume high levels of energy. Furthermore, the Saudi Energy Efficiency Center (SEEC) has initiated efforts to improve energy consumption in existing buildings, including enhancing the energy efficiency of household appliances and lighting products []. However, further investigation is needed to understand the energy-saving potential of energy upgrade measures for existing buildings. Upgrading the building envelopes is essential for reducing energy consumption, which can reach up to 68% []. However, retrofitting existing buildings for energy efficiency can be challenging due to design limitations, climate considerations, and occupant behavior []. Unfortunately, the retrofitting progress of existing buildings has been slow due to insufficient awareness, funding, and technical expertise [].
This paper aims to evaluate the energy-saving potential of building envelope upgrades through energy retrofitting measures for a case study of a residential building in Jeddah, Saudi Arabia. The study compares various strategies for eight apartments to illustrate the potential energy savings achievable for each unit. The building under investigation is a typical low-rise residential structure, consisting of five floors and eight apartments. Key performance indicators, such as the average annual energy consumption (AAEC), are used to compare the energy consumption of the building before and after the energy retrofit scenarios. The proposed scenarios encompass improvements to the building envelope and HVAC systems.
The study explores the challenges and opportunities associated with retrofitting existing buildings in Jeddah city, emphasizing the importance of a holistic approach that considers multiple factors when retrofitting existing buildings. Additionally, the potential for energy savings resulting from retrofitting existing buildings is examined. The paper also discusses uncertainties and their effects on the AAEC, including the infiltration rate (ACH50) (Air changes per hour at 50 Pascal) and the desired user thermal comfort temperature. The simulation of energy from the basic model employed an infiltration rate of 20 ACH50, with attempts made to generate scenarios targeting a rate of 4 ACH50, as required by SBC standards. Outdoor scenarios were applicable to the entire building, rather than individual apartment upgrades. The simulation results reveal a significant reduction in the average annual energy consumption (AAEC) when a deep energy retrofit scenario was implemented.
The paper’s conclusion compares the energy-saving potential among different scenarios and highlights certain uncertain factors that impact the AAEC, such as infiltration rates and setback temperatures. The study’s results contribute significantly to the existing literature on energy retrofitting of existing buildings in Jeddah.

2. Methodology

A mixed-methods approach, encompassing qualitative and quantitative techniques, was employed in this study to provide comprehensive results contributing to the existing literature on energy retrofitting of existing buildings in Jeddah. Qualitative data were collected from the relevant literature and related studies, forming a solid foundation for exploring the energy-saving potential of retrofitting measures using the DesignBuilder (Version 7) digital software (EnergyPlus).
To examine the energy-saving possibilities and enhance the energy efficiency of existing apartments in Jeddah, a series of energy retrofitting scenarios were analyzed for eight apartments within the same building. The simulation tool was utilized to assess the current energy consumption and determine the potential energy savings for each scenario.
The objective of this paper was to evaluate and compare the energy-saving possibilities across different energy retrofitting scenarios, utilizing simulation software. To establish the simulation parameters, several steps were undertaken. First, a comprehensive review of the literature and related studies was conducted to summarize the essential design parameters and energy benchmark levels, which play a crucial role in highlighting specific variables. Second, the case study was described, as it was necessary for digital modeling. Data were collected from various sources, including floor plans, apartment orientation, component materials and U-values, user activities, and mechanical AC systems. Third, specific uncertainties that affect energy savings results, such as the infiltration rate and user thermal comfort (setback temperature), were identified. Fourth, an overview of the energy upgrade scenarios and interventions was formulated. Finally, the simulation results were analyzed to evaluate and compare the energy savings achieved by each energy upgrade scenario.
Further illustrations will be presented in subsequent sections, and Figure 1 provides a visual representation of the workflow, facilitating readers’ understanding of the sequential steps in this study.
Figure 1. Research structure of the paper.

4. Design Parameters and Energy Benchmark

This section aims to define the design parameters and energy consumption benchmark levels to establish the study scope and enable energy upgrade scenarios.
Key performance indicators (KPIs) are crucial for evaluating the scenarios. The primary KPI is the Annual Average Energy Consumption (AAEC) in kWh/m2/year, which serves as a benchmark for comparing various upgrade scenarios to the original case. The evaluation of each apartment considered at least one of the following upgrades: U-values (W/m2K), thickness (cm), infiltration rate (ACH50), Solar Heat Gain Coefficient (SHGC) and Window-to-Wall Ratio (WWR) for windows, and Coefficient of Performance (COP) for AC units.
The hot, arid, humid climate conditions in Jeddah necessitate mechanical systems in all indoor spaces, a finding supported by other researchers, such as Felimban and Alaidroos [,]. In addition, it is essential to consider the building location within the neighborhood, as this could also affect the AAEC for each apartment in the building. Additionally, the location of the building within the neighborhood must be taken into account, as it can also impact the AAEC for each apartment. The selection of building types was based on the number of housing units and the new buildings developed by the KSA Ministry of Housing. Apartments account for more than 50% of the total housing units in KSA [].
This research focused primarily on buildings constructed using the concrete skeleton structure (CSS), as this is the predominant construction method in Jeddah. The historical aspect of the buildings was not considered. Moreover, the construction process of a CSS involves wall infilling with blocks, plaster/cement finishing, and aesthetical finishing. The Saudi Energy Efficiency Centre (SEEC) and Felimban highlight that over 70% of residential buildings in Jeddah lack thermal insulation, underscoring the need for energy retrofit upgrades of the existing building envelopes [,].
The Saudi Building Code (SBC) has upgraded its energy efficiency requirements and energy benchmarks. However, in February 2022, the SBC National Committee revised the energy efficiency requirements in response to feedback from construction companies and their reluctance to issue new construction permits for residential buildings. Table 11 illustrates the specific value changes in energy requirements resulting from the SBC upgrades by the National Committee.
Table 11. SBC energy requirements upgrades (Bold numbers indicate the changes) [,,,].
Aldossary’s research established AAEC values for different residential buildings in KSA, albeit only covering the first two floors of low-rise residential building types [].
Unfortunately, the top floors of buildings require more extensive energy upgrade interventions to achieve better performance, as they are more exposed to the sun’s heat and radiation due to additional external surfaces. Researchers have observed AAEC values ranging from 116 to 165 kWh/m2/year, which are predicted to be even higher for top floors. However, Aldossary proposed AAEC values in the range of 77–98 kWh/m2 to achieve a low carbon energy consumption level [].
Various researchers, including Aldossary, Alaidroos, Krarti, and Hijazi, have explored different sets of energy retrofit measures that can reduce energy consumption in the residential building sector by 37%, 41.5%, 50%, and up to 80% when applying hybrid systems [,,,]. The literature often presents optimistic predictions of energy savings for existing buildings when implementing different energy-saving measures. In this study, detailed energy retrofit scenarios have been defined to provide a more realistic estimation of energy-saving possibilities for specific units in Jeddah. Additionally, factors such as infiltration rate (ACH50) and user thermal comfort temperature (°C) have been included, as they impact the AAEC results. However, the calculation did not consider thermal bridges due to the complexity of methodologies required to obtain accurate results [].

5. Case Study Descriptions and Simulation Process

5.1. Building Location and Position Selection

The selected case study was the residential building described in detail in Section 3.4. Jeddah’s climate and location have been described in considerable detail in Section 3.4 and by Felimban, Talep, and Aldossary [,,]. The building position that was eventually selected was based on simulation testing of six positions of a typical building in an urban setting. Then, the worst case was selected, where the average energy consumption was the highest. This will be further shown in the simulation progress section.

5.2. Building and Apartment Descriptions

Generally, the land area for a residential building varies among 20 m × 20 m, 20 m × 30 m, 25 m × 30 m, and 30 m × 30 m, with a built-up ratio maximum of 60% [,]. The building case was extracted from actual plans of a low-rise residential building (4 floors) provided by an architectural firm []. However, the case is based on a land size of 750 m2 (25 m × 30 m), resulting in a built-up floor area of around 450 m2. The selected building contains eight apartments (two per floor), and the first floor (ground floor) has parking spaces and other services, such as driver rooms and the main entrance. The apartments mainly face either west or east. However, the east and north sides face the neighboring buildings, while the west and south sides face the street. These factors have an effect on the AAEC for each apartment. Each apartment has three bedrooms, a living room, a kitchen, a dining room, a reception room, a maid room, and three bathrooms, as shown by the floor plans in Figure 13, Figure 14 and Figure 15.
Figure 13. First floor plan (ground floor) (14 Parking spots, 6 Driver rooms, and 1 guest room).
Figure 14. Repeated floor plan (3 bedrooms, 1 maid room, 1 kitchen, 1 living room, 3 bathrooms, 1 dining room, and 1 guest room).
Figure 15. Roof floor plan.
The building fabric was defined and illustrated based on previous studies and material properties. Table 12 and Table 13 demonstrate every component in respect to total U-values, component thickness, and other variables. The apartments on the east side of the building have the same floor area, which is around 215 m2, while on the west side, the area is around 225 m2.
Table 12. Building specifications.
Table 13. Building fabric description and current energy values of building components.

5.3. User Profile

In the real world, every apartment has a different user profile, while in this example, specific information has been used to create a basis, against which other apartments can be compared. The typical number of users in an apartment is 7, including a housemaid; the average family size is 5.9 members [,]. The activity in the apartment varies depending on the parents’ professions. However, in this study, it is assumed that user activities are based on a proposed schedule of activities and AC working duration hours, as demonstrated in Table 14. Furthermore, every room has a different number of hours during which the AC is used; the living room proved to be the most active room, with usage of 17.5 h per day, and the guest room was the least active room, using an average of 3 h per day, as Figure 16 illustrates.
Table 14. User activity schedule for a case model of a Saudi Family.
Figure 16. Comparison of the average AC duration for different rooms per day.
The provided assumed activity hours were the minimum duration hours that varied among families. However, a compact schedule, i.e., a schedule where the people who lived in the house were there for the maximum number of hours, was taken as the basis to use later in the simulation program (DesignBuilder). The assumed schedule was applied to all the apartments to provide comparable numbers that could subsequently be validated. The occupancy percentage was 20% during the inactive hours (07:00–16:00).

5.4. Building Ownership

The ownership of a residential building was primarily only single ownership until it developed into a multi-ownership model. In 2018, the “Mullak” ownership system was introduced to settle the required rules for single- and especially multi-ownership types of apartments []. In this study, the ownership of a building has a significant role in designing the energy retrofitting scenarios, which have been divided into single-ownership or multi-ownership types.
Typically, the construction of any residential building falls within three types of constructors: individual, private developer, or governmental. Each type has different business activities that fulfil the construction’s primary goal. Therefore, the type of ownership falls under single- or multi-ownership, as Table 15 illustrates.
Table 15. Different business activities for several building contractors.
The energy retrofitting scenarios have been divided into two primary types: indoor and outdoor. The indoor scenarios are possible for both ownership types, while the outdoor scenarios are only possible for the single-ownership type because of difficulties in the decision-making processes.

5.5. Simulation Description

The selected software was DesignBuilder [], which allows engineer researchers to analyze the energy consumption of building energy. However, a comparative study of widely used dynamic simulation tools for buildings, such as EnergyPlus, TRNSYS, Simulink libraries CarnotUIBK and ALMABuild, IDA ICE, Modelica/Dymola, and DALEC, demonstrated a good consensus among these tools, despite the varying levels of input detail required by each tool []. The Design-Builder tool was chosen due to its availability in the market and its accessibility as a simulation software. It allows for the analysis and prediction of energy consumption in any structure using predefined datasets. The Design-Builder program is particularly user-friendly, making it suitable for educational purposes. It eliminates the need to extensively delve into software details and codes. The main features of using the Design-Builder software are its ability to simulate accurate environmental performance data, its fast simulation capabilities, and its ability to import various file types for 2D and 3D imaging. Additionally, one can save rendered images of any result at any stage [,].
The study modelled the case study in the DesignBuilder software using the collected actual floor plans from the Archteam firm. The data were entered based on previous studies described earlier in this section.
Initially, the floor plans were extracted from the provided documents, and a 3D model was constructed using the DesignBuilder software. The wall specifications were then added based on Table 13, which was derived from Table 5 and other relevant literature. Subsequently, the window and roof specifications were incorporated. Afterward, various datasets were inputted, including ACH50 (N50), setback temperature, climate data, and activity data. The simulation was then conducted to obtain annual energy consumption data, which were stored in an Excel file. The simulation covered 8 apartments, each with 17 scenarios (10 indoor and 7 outdoor), resulting in a total of 272 simulations per trial.
Due to various uncertainties, the simulation was repeated multiple times, accounting for factors such as the actual infiltration rate and the AC setback temperature, which are further elaborated upon in the subsequent sections. Each scenario’s simulation time ranged up to 7 s. The primary objective of using AAEC (Annualized Average Energy Consumption) was to compare the energy consumption before and after implementing the upgrading measures for all eight apartments within a single building.

6. Energy Retrofitting Scenario Description

The available energy retrofit interventions were described in the previous study as a guideline for designing the energy retrofitting scenarios in this section. Table 16 illustrates every energy upgrade scenario, as it shows the interventions used. In addition, Table 17 and Table 18 illustrate the scenarios designed to achieve the SBC requirements, where red colors mean that the value did not meet the SBC energy requirements, while green means that the value did meet the SBC energy requirements. The concept achieves high-resolution scenarios by starting with minimal changes and adding additional intervention to reach an efficient scenario that meets the SBC (green labels in Table 17 and Table 18). The design was divided into two categories, indoor scenarios and outdoor scenarios, and these are described as follows.
Table 16. Overview of indoor and outdoor scenarios. Detail for the scenario construction in Table 17 and Table 18.
Table 17. Indoor energy retrofit scenarios for a residential building in Jeddah (red color indicates did not meet the SBC and green color indicates the value meets the SBC).
Table 18. Outdoor energy retrofit scenarios for a residential building in Jeddah (red color indicates did not meet the SBC and green color indicates the value meets the SBC).

6.1. Indoor Scenarios

In Table 17, Scenario 1 involves the replacement of windows with an energy-efficient option. Scenarios 2–5 incorporate additional measures to enhance wall insulation with local materials to achieve the required SBC U-values. Scenarios 6 and 7 incorporate the wall upgrade aspect of Scenario 5, with the window replacement, while the only difference between Scenarios 6 and 7 is the type of windows used. Scenarios 8 and 9 follow the approach of Scenario 7 and upgrade the roof U-value with two distinct U-values. Finally, Scenario 10 builds upon Scenario 8 and replaces the air-conditioning systems with efficient alternatives.

6.2. Outdoor Scenarios

In Table 18, Scenarios 1 and 2 incorporate external insulation and finishing systems (EIFSs) as add-on measures to improve the U-value of the walls. Scenarios 3 and 4 build upon Scenario 2 and replace the windows. Scenarios 5 and 6 follow the approach of Scenario 4, including upgrading the roof U-values. Lastly, Scenario 7 incorporates the measures from Scenario 5, but also involves replacing the air-conditioning systems with energy-efficient alternatives.
Table 17 and Table 18 demonstrate how and what the scenarios are. The central concept of designing the energy retrofit scenarios was to develop scenarios from a minor upgrade to a deeper upgrade using mixed energy retrofitting strategies (add-in, add-on, replace-it, and wrap-it) in order to reach the SBC energy requirements. The scenarios are intended to develop the targeted envelope component (wall, windows, and roof) to upgrade the heat-resistant value in order to achieve better performance. In both Table 17 and Table 18, the green color indicates that the value reached the SBC minimum standards.
The infiltration rate was assumed as 20 ACH50, as recommended by Makawi, where higher results could be possible for the basic case [,]. The rationale for employing a value of 20 ACH50 to represent infiltration in simulation software is based on several factors. ASHRAE defines infiltration as the unintended flow of outdoor air into a building through cracks, openings, and exterior doors []. Airtightness is a related concept, referring to the amount of air infiltrating a building at a pressure difference of 50 Pa []. Infiltration and airtightness are distinct but related phenomena, with empirical evidence suggesting that infiltration is typically around 1/20th the value of airtightness [].
The blower door test (BDT) is commonly used to measure airtightness by measuring air change rates under a 50 Pa pressure difference []. The resulting value, known as ACH50, is a measure of the infiltration of outdoor air into a building and is influenced by envelope tightness. Infiltration can contribute significantly to a building’s heating and cooling loads, with estimates ranging from 25% to 50% in some studies [,]. Research has shown a wide range of ACH50 values in residential buildings, with values as high as 39 ACH50 in some cases []. However, the exact value will vary depending on various factors, including the type of window frames used.
In Saudi Arabia, a study found a lack of infiltration data on the building stock and recorded ACH50 values of 6.58 and 7.04 for 2 houses in Dhahran City []. These values were due to exfiltration caused by the central HVAC fan system. This study and other literature show that 20 ACH50 is not considered high for an existing residential building.
To validate the proposed energy retrofitting scenarios, a value of 20 ACH50 is used for the basic case model to improve this value to 4 ACH50, as required by the Saudi Building Code for the airtightness of residential buildings in Jeddah. This approach aligns with previous research recommendations and is based on a range of empirical evidence.

7. Results and Analysis

The energy performance simulation process follows three steps. The first step explores the highest average energy consumption of a residential building using different urban positions. The second step shows the different energy consumption results when the variables have been changed, such as the infiltration rate or how the temperature in the various rooms is controlled, which will later affect the possible energy-saving results. The third step involves performing an energy simulation for each proposed scenario in order to calculate the potential energy savings. Hence, every step will provide significant information that will help analyze the simulation results using different variables.

7.1. Step One: Building Position (Locating the Highest Energy Consumption)

In the KSA context, it is possible for a residential building to be situated in six different positions when the alone (no surrounding buildings) position faces towards the south (see Figure 17). The southwest (SW) position (see Figure 18) recorded the highest AAEC compared to other positions, as shown in Figure 19. The north position is found to have almost the same AAEC as 180 kWh/m2/year (the value is total average energy consumption of all apartments in one building), as shown in Figure 19 and Figure 20.
Figure 17. Rendering of a residential building (south orientation).
Figure 18. Rendering of a residential building (southwest orientation) within other buildings.
Figure 19. Comparison of energy consumption for different building positions that face south.
Figure 20. Comparison of energy consumption for different building positions that face north.
Note that the apartments switched sides when the building switched from north to south orientation. At the apartment level, the AAEC increased from ground-level to top-floor apartments, requiring additional energy-saving interventions in the designing stage (see Figure 19 and Figure 20).

7.2. Step Two: Effect of Changing Infiltration Rate and Cooling Temperature on AAEC

The infiltration rate ACH50 is crucial in determining the AAEC. In this study, ACH50 values of 4, 6, 7, 8, 15, 30, and 50 were considered, with 4 ACH50 considered best practice, according to the SBC []. The maximum ACH50 value of 50 was determined based on previous studies that found a maximum of 39 ACH50 through monitoring methods []. This study includes the infiltration rate and its impact on the AAEC, with results demonstrating the significance of the ACH50 on energy consumption for each scenario and apartment. The study used 20 ACH50, calibrated with the average energy consumption bill, as reported by Aldossary for the first 2 floors of the building []. Hence, different infiltration rates (50 to 4 ACH50) were tested, and when applying lower infiltration rates, lower AAEC results were achieved. Figure 21 demonstrates a range of increases in AAEC when only changing the infiltration rate from 50 to 4 within the same apartment. The AAEC increase percentages range from 62% to 74%, as the top-floor apartments with higher ACH50 had the highest values compared to the lower floors.
Figure 21. Changes in the impact of the ACH50 rate on AAEC for every apartment in south orientation case.
The user comfort level is another factor affecting the AAEC, as cooler temperatures increase energy consumption, requiring extra cooling. The thermal comfort preferences of occupants in Jeddah vary, with a typical cooling temperature range of 19–24 °C, according to Felimban []. The scenarios for changing cooling temperatures highlight the impact on the AAEC. For example, as shown in Figure 22, the AAEC for apartment 1 decreases by approximately 4 kWh/m2/year when the cooling temperature is increased by 1 °C. However, decreasing the cooling temperature by 2 or 3 °C increases the AAEC by around 15, 33, or 51 kWh/m2/year. However, a lower cooling set-point temperature leads to a higher AAEC in air-conditioned apartments. To conclude, both the infiltration rate and user thermal comfort levels are considered primary impact factors that contribute to the increase or decrease in the AAEC, as shown in Figure 10 and Figure 11.
Figure 22. Impact of changing the setback point of the cooling temperature on AAEC using DesignBuilder simulation in south orientation case.

7.3. Step Three: Energy Performance Simulation and Energy Savings

The energy simulation of the basic model used 20 ACH50 infiltration rates and aimed to produce scenarios targeting a rate of 4 ACH50, as the SBC standards require. According to the simulation results, Figure 12, Figure 13, Figure 17 and Figure 18 illustrate the AAEC for each apartment using infiltration rates of 20 and 4 ACH50. The following two sections illustrate the AAEC results that depend on the user scenario and the selected infiltration rate. The simulation was divided into indoor and outdoor scenarios, as explained earlier in the description of the scenarios.
A
—Indoor Scenarios
As previously explained, indoor scenarios can be applied individually to any apartment. The simulation results show an extensive reduction in AAEC when using a deep energy retrofit scenario (Scenario 10); the reduction was up to 121 kWh/m2/year. When applying a minor retrofit scenario (Scenario 2), it was possible to reduce the amount of electricity used by at least 34 kWh/m2/year compared to the basic model. In addition, the AAEC varied from one apartment to another depending on the apartment position (floor level) and the apartment orientation in the building. All of the deep retrofit scenarios led to a more efficient AAEC for all apartments.
The most critical factor of AAEC reduction was the insulation upgrades for the walls and roofs. Adding an insulation layer to the walls and roofs resulted in a significant sharp reduction in energy use, as shown in Figure 23 and Figure 24. Additional upgrade interventions, such as window replacement and roof upgrade, added further reduction to the energy consumption with different percentages.
Figure 23. AAEC values for indoor energy retrofitting scenarios using 4 ACH50 for infiltration rate.
Figure 24. AAEC values for indoor energy retrofitting scenarios using 20 ACH50 for infiltration rate.
It was also shown that window and AC upgrades greatly influence the ACH50 rate, where outdoor heat is prevented from penetrating through the air gaps to the indoor space.
Energy savings gradually increased from Scenario 1 (5–10%) to Scenario 10 (45–56%), where the infiltration rate was 20 ACH50, while for the 4 ACH50 infiltration rate, Scenario 1 (6–12%) to Scenario 10 (55–65%) are illustrated in Figure 25 and Figure 26. There were remarkable differences in energy savings between apartments when applying the different Scenarios (1, 2–5, 6–7, and 8–10).
Figure 25. Possible energy savings from testing different scenarios (indoor) where the infiltration rate is 20 ACH50.
Figure 26. Possible energy savings from testing different scenarios (indoor) where the infiltration rate is 4 ACH50.
Apartments 7 and 8 recorded around 60% savings when using Scenarios 8, 9, and 10, where additional insulation was added to the roofs. However, apartments 1–6 only had a slight savings increase when applying Scenarios 8, 9, and 10 compared to Scenarios 6 and 7. Apartments 7 and 8 had less energy savings than apartments 1-6 when using Scenarios 1–7. Therefore, it is suggested that every apartment has specific properties that require different energy retrofitting scenarios, and an individual cost analysis per apartment is required.
Furthermore, more energy savings were achieved when the basic model used 20 ACH50 and the applied scenarios used 4 ACH50. The energy savings increased for Scenario 1 from 5–10% to 17–26%, and for Scenario 10, they increased from 45–56% to 63–65%, where the change in the ACH50 rate had a significant impact on the energy savings percentage (see Figure 27). The considerable energy savings show the importance of considering infiltration rate levels in energy retrofitting applications to achieve a better AAEC for all apartments.
Figure 27. Possible energy savings from testing different scenarios (indoor) where the infiltration rate is 4 ACH50, with a rate of 20 ACH50 for the basic model.
The indoor scenarios are very valuable for individual decision making for energy retrofit upgrades. The only concern in these indoor scenarios is the thermal heat transfers through the concrete skeleton structure (thermal bridges), especially when the structure intersects with an indoor partition. In this study, thermal bridges have not been incorporated in the calculations, as the main objective of the study was to calculate the overall energy-saving possibilities so that the factors could be easily calculated in the future in order to help retrofit the residential buildings and ensure energy efficiency.
In summary, the indoor scenarios of energy retrofitting applications have great potential to enhance the energy efficiency of residential apartments, with energy savings ranging from 20% to 65% depending on the apartment’s circumstances.
B
—Outdoor Scenarios
The outdoor scenarios, as observed earlier, can only be applied to the whole building and cannot be applied to individual upgrades to individual apartments. The simulation results show a sharp reduction in AAEC when using a deep energy retrofit scenario, as can be seen with Scenario 7 shown in Figure 28 and Figure 29. However, adding 10 cm of insulation to the outdoor wall, as shown in Scenario 1, can significantly reduce at least 50 kWh/m2/year compared to the basic model. Figure 28 and Figure 29 illustrate significant reductions in AAEC, each using different infiltration rates of 20 ACH50 and 4 ACH50.
Figure 28. AAEC for outdoor energy retrofitting scenarios using 20 ACH50 for the infiltration rate.
Figure 29. AAEC for outdoor energy retrofitting scenarios using 4 ACH50 for the infiltration rate.
To provide more detail, Figure 28 presents different ranges of decrease of the AAEC depending on the apartment and the applied scenario. The AAEC results for apartment 1 show a 33% reduction for Scenario 1 and a 46% reduction for Scenario 7. However, apartment 8 records an 18% reduction for Scenario 1 and a 55% reduction for Scenario 7.
Apartments 1–6 gradually increased their energy savings when applying the scenarios in order, as Figure 30 and Figure 31 illustrate. Apartments 7 and 8 had less energy savings when using Scenarios 1–4 compared to the other apartments. However, outdoor Scenarios 5–7 significantly increased the energy savings for apartments 7 and 8. Generally, the high-resolution scenarios depend on the infiltration rate levels and the selected scenario.
Figure 30. Possible energy savings from testing different outdoor scenarios where the infiltration rate is 20 ACH50, with a rate of 20 ACH50 for the basic model.
Figure 31. Possible energy savings from testing different scenarios (outdoor) where the infiltration rate is 4 ACH50, with a rate of 4 ACH50 for the basic model.
Figure 31 indicates more promising energy savings for all units when applying scenarios that include improving the infiltration rate to 4 ACH50 compared to the results in Figure 31. Figure 32 shows decreasing savings percentages from 50% to around 30% for apartments 1 and 8, respectively. However, if any of Scenarios 5–7 applied to all apartments 1, 2, 3, 4, 5, 6, 7, and 8, then AAEC could reach efficient consumption values of 52, 55, 61, 63, 66, 68, 75, and 76 kWh/m2/year, respectively.
Figure 32. Possible energy savings from testing different scenarios (outdoor) where the infiltration rate is 4 ACH50, with a rate of 20 ACH50 for the basic model.
In summary, the simulation results of the energy performance for residential buildings in Jeddah validated an optimistic range of energy savings (30–60%) when applying different energy retrofit scenarios.

8. Discussion

The discussion has been divided into three main points. Initially, the AAEC is discussed in respect to the eight apartments, based on the analyzed properties; secondly, the energy-saving possibilities are discussed in respect to applying different scenarios; finally, the uncertainties and the effects on the AAEC are addressed, such as the infiltration rate (ACH50) and the user thermal comfort temperature.

8.1. Average Annual Energy Consumption

The simulation results for residential apartments range from 145 to 221 kWh/m2/year, depending on the orientation and the floor level. Apartments situated on the upper floors consume more AAEC than apartments found lower in the building due to the heat exposure from the roof. For instance, apartments 7 and 8 recorded the highest AAEC of 216 and 221 kWh/m2/year.
The apartments that faced the west recorded a higher AAEC than east-facing apartments when they were located on the same floor. In addition, 2 west-facing apartments, i.e., apartments 2 and 4 (161, 166 kWh/m2/year), consumed more than the upper floor, east-facing apartments 3 and 5 (152, 163 kWh/m2/year). The apartment location, specifically the orientation and floor level, are the main factors used to calculate the AAEC.

8.2. Energy-Saving Possibilities

In general, the simulation results demonstrate a significant impact from every scenario. The impact degree is based on the weaknesses in the envelope component design, such as the walls, the windows, or the roof. Furthermore, in respect to apartments 1–6, the weaknesses came from the walls and the windows, where different energy savings were recorded from Scenarios 1–7, ranging from 7% to 47%, whereas Scenarios 8–10 only add about 2% savings compared to Scenario 7.
The weaknesses in apartments 7 and 8 were due to all components, and the roof presented the main weakness. For instance, apartment 8 had energy savings when applying Scenarios 1–7, ranging from 6% to 26%, and 55% to 56% for Scenarios 8–10.
Every scenario has energy-saving possibilities, leading to better energy performance to achieve the main objective of extensive simulation validation.

8.3. Uncertainties

Uncertainty factors affect the AAEC, such as the actual infiltration rate and the user’s thermal preferences (user thermal comfort). Each factor dramatically influences the AAEC as they can increase the energy-saving possibilities when they are known before designing the possible energy scenarios.
An actual infiltration rate (ACH50) is a significant factor that can be used to demonstrate actual energy savings, as Figure 33 illustrates. It is also important to note that the savings percentage increased when the infiltration rate was enhanced.
Figure 33. Possible energy savings when different infiltration rates were applied.
The existing residential buildings in Jeddah, KSA, currently require an air conditioning system every day of the year when an infiltration procedure occurs. If the infiltration rate is tested, then the air tightness of the indoor spaces could be designed better in the energy retrofitting scenarios.
The other factor is the difference in user thermal comfort. User thermal comfort varies from family to family. However, both the infiltration rate and cooling temperature affect the increasing possibility of AAEC for all apartments, as Figure 34 illustrates. Understanding the user’s thermal comfort would help designers and occupants to lower their energy usage; increasing designers’ awareness so that thermal comfort is considered in the design process is very important. In short, higher cooling temperatures and lower infiltration rates lead to extensive energy savings.
Figure 34. Impacts of cooling temperature and infiltration rate change on AAEC for all apartments.

9. Conclusions

The energy retrofit scenarios presented in this study were validated through digital simulation using DesignBuilder software to demonstrate the potential for energy savings. The baseline case model yielded AAEC values for apartments 1, 2, 3, 4, 5, 6, 7, and 8, respectively, of 145, 161, 152, 166, 163, 174, 216, and 221 kWh/m2/year. However, it is important to note that the building’s location within the urban environment influences the AAEC for all units. Additionally, the position of each apartment (orientation and floor level) results in different AAEC values.
This paper has presented a comprehensive case study that considers crucial elements, such as building location, apartment positioning, user profiles, and ownership types. Two energy-upgrade scenarios, focusing on indoor and outdoor improvements, were introduced for the eight apartments. These scenarios primarily involve upgrading building components (walls, windows, and roofs) to meet the energy benchmark level defined by the upgraded SBC energy standards. The outcomes of the analysis provide insights into key variables that can significantly impact energy savings. It is worth noting that achieving the highest energy savings depends on various factors, including interventions for improving the building envelope, enhancing the infiltration rate, and determining the desired level of thermal comfort. While the simulation encompassed different design variables, two main variables (infiltration and user thermal comfort level) can yield more accurate AAEC values if known during the scenario design phase.
However, it is important to consider not only energy savings, but also the cost aspect, when selecting the optimal scenario. Evaluating the cost associated with each scenario is crucial to determine its suitability for individual cases. This aspect will be further explored in a subsequent paper, providing a more comprehensive understanding.
In conclusion, based on the analysis of energy retrofit scenarios in Jeddah, a series of simulations was conducted to confirm the potential for energy savings, ranging from 25% to 66%. The findings emphasize the significance of implementing energy-saving measures and highlight the opportunities for improving energy efficiency in the residential building sector.

Author Contributions

Conceptualization, A.F., U.K. and T.K.; Methodology, A.F., U.K. and T.K.; Validation, A.F.; Formal analysis, A.F.; Writing—original draft, A.F.; Writing—review & editing, A.F.; Visualization, A.F.; Supervision, U.K. and T.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no financial support for the research, authorship, and/or publication of this article.

Conflicts of Interest

The authors declare no conflict of interest with respect to the research results, authorship, and/or publication of this article.

Nomenclature

List of abbreviations
AAEC Average Annual Energy Consumption
ACH Air Change per Hour
ACH50 Air Changes per Hour at 50 pascals pressure differential
CDD Cooling Degree Days
COPCoefficient of Performance
CSS Concrete Skeleton Structure
EIFSExternal Insulation Finishing System
EPS Expanded Polystyrene Insulation
ERA Energy Retrofitting Application
GCC Gulf Cooperation Council
KPI Key Performance Indicator
KSA Kingdom of Saudi Arabia
SBC Saudi Building Code
SEEC Saudi Energy Efficiency Centre
WWR Window-to-Wall Ratio
XPS Extruded Polystyrene Insulation

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