Multi-Criteria Decision-Making for Selecting Solar Window Film Sheets for Energy Saving in Buildings

Abstract

Recently, there have been several advancements in the field of sustainable energy solutions, particularly in the selection of solar window film sheets. In this research, a multi-criteria decision-making approach was applied to compare three different types of window film sheets, Silver 35, TrueVue 15, and Sterling 40, to aid in selecting the most suitable window film based on the United Arab Emirates market. The primary aim of this work is to provide decision-makers with a structured approach to enhance their choices for selecting window film sheets. The methodology employed involves evaluating various criteria, including visible light transmittance, solar energy rejected, energy transmittance, energy absorptance, cost, glare reduction, visible light reflectance interior, and fade reduction. These criteria are assessed using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results demonstrate that Sterling 40 is the best choice followed by Silver 35. Based on the final TOPSIS results, the difference between the scores of these two window film sheets was not significant, while they were far from the score of TrueVue 15.

1. Introduction

In recent years, the increasing focus on sustainable energy solutions has led to significant advancements in the field of energy saving in buildings. Various technologies and innovations have revolutionized the way buildings consume and conserve energy, including smart windows [1], high-performance insulations [2], energy-efficient HVAC systems [3], renewable energy integration [4], and passive designs [5]. Solar window film sheets, in particular, have emerged as a promising option to benefit from sunlight while reducing solar energy transmittance [6,7,8]. However, the selection of the most suitable window film sheet involves a complex decision-making process due to various factors such as cost, efficiency, durability, and environmental impact. Given the increasing number of companies worldwide producing window films, it has become crucial to select the most suitable option for commercial office use.

Previous research in the field of sustainable building design and energy efficiency has emphasized the significance of selecting appropriate solar control measures, including window film sheets, to optimize energy performance and indoor comfort. Studies have highlighted the impact of different window film specifications like visible light transmittance, energy transmittance, and thermal resistance. The energy savings from installing solar window films were investigated by Yin et al. [9] in a commercial building in Shanghai, China. The yearly performance of a building with and without the window film was simulated utilizing eQUEST. The outcomes of the simulation showed that the impact of the window film was greatly affected by the original glazing system’s configuration and the location of the window film. It was reported that the film could reduce the solar heat gain and shading coefficients by 22% and 44%, respectively, when applied externally.

In [10], the glass surface temperature was measured for selected window films on different applications and compared to a window without films. Dynamic simulation models were developed using EnergyPlus, which were employed to calculate the possible energy savings from window films installed on glazing in three distinct rooms in Hong Kong: office, shopping mall, and hotel guest room. The findings showed that the thermal performance of the films on clear glass is superior to tinted or laminated glass. Window films exhibited significant energy-saving potential in all three commercial building areas, with the best outcome in the office room. Oh et al. [11] proposed two remodeling techniques utilizing polymer-dispersed liquid crystal (PDLC) films that can control solar radiation in old office buildings. In their study, they employed EnergyPlus to analyze the performance improvement and daylight effectiveness based on the remodeling. The first technique included adding a PDLC film to the original double glazing curtain wall. The second technique included setting up a separate frame, laminating a PDLC film between two glass substrates, and attaching a new window to the interior side. According to their findings, the building’s energy consumption was reduced by 3.1–17% and 18.8–22.4% considering the first and second methods, respectively.

In [12], a modern office in the United Arab Emirates (UAE) was chosen to assess the practicality of solar window film and its impact on room lighting. The evaluation used two methods: field measurements and a questionnaire survey. The study revealed that a solar window film with 50% visible light transmittance could reduce energy consumption by up to 33%. Huang et al. [13] assessed the impact of window films on interior conditions and air conditioning power usage. The performances of two films were evaluated, heat-absorbing film and reflective film, considering differences in the building’s orientation. When facing the east, south, west, and north-west directions, in comparison to the clear glass the heat-absorbing film’s electricity savings were 1.4%, 1.9%, 1.4%, and 1.2%, respectively, while those of the reflective film were 3%, 4.2%, 4.2%, and 10.3%, respectively. Moghaddam et al. [14] investigated the effects of low-emissivity window films on energy efficiency and thermal comfort in a three-story stone building in Sweden’s cold conditions utilizing simulation and on-site measurements to determine the corresponding thermal and optical characteristics. They found that applying the low-emissivity film to the outer surface of the inner pane of double-glazed windows reduced winter heat loss and summer heat gains by 35–36%. This led to a 6% decrease in the yearly heating consumption of the building and lowered the total occupant hours with thermal dissatisfaction from 14 to 11 percent. A transparent radiative cooling (TRC) film, designed with low transmittance in the solar spectrum and selectively high emissivity in the atmospheric window (8–13 μm), was utilized on roof glazing to enhance building energy efficiency, as described in [15]. To assess the TRC film’s effectiveness, two identical model boxes were set up, and their internal air temperatures were recorded in Ningbo, China, during August. The study found a maximum temperature difference of 21.6 °C between the boxes with and without the TRC film. By considering the TRC film’s cooling effect in summer and heating impact in winter, the building’s annual air conditioning energy consumption could be reduced by 40.9–63.4%, depending on varying climate conditions. Shaik et al. [16] examined the solar optical characteristics and air-conditioning cost-saving potential of different smart PDLC film glasses. They presented the characteristics of four types of PDLC film glasses with and without voltage applied, developing a numerical model to evaluate solar heat gain in these states and estimate yearly cost savings. All smart PDLC films showed significant heat reduction compared to clear glass in buildings across three climates. The white smart PDLC film glass proved to be the most energy-efficient, offering the greatest cost savings (USD 101.76/year in hot and dry climates) and the shortest payback period (12.71 years), while maintaining adequate daylight factors. In [17], a smart window component using thermo-responsive hydrogel for dynamic solar modulation was designed, suitable for both new buildings and retrofits. The hydrogel film incorporated an IR-reflective coating to enhance solar regulation. While the coating slightly reduced the hydrogel’s total light modulation by 0.9–5.4%, it effectively blocked additional IR radiation, making it ideal for hot climates with high solar exposure. Annual energy simulations for a commercial building prototype in Tucson, AZ, demonstrated that the hydrogel window component could achieve up to 8.1% annual space cooling energy savings, equating to 30.6 kWh/year·m². Somasundaram et al. [18] focused on retrofitting double glazing for older buildings and suggested additional measures to enhance energy savings. These measures include installing solar films before the retrofit, optimizing natural daylight, and actively scheduling air-conditioning and lighting systems. They found that combining solar films with retrofit double glazing could reduce annual HVAC energy consumption by up to 20%. Further, active scheduling of compressor operation can save an additional 7%, and adaptive lighting control can raise total savings to 17%. Overall, these cost-effective retrofits can reduce a building’s annual electricity use by 30–40%.

The existing literature underscores the importance of adopting a systematic approach to selecting window film sheets in building design. However, all reported studies have investigated the effect of different window films on building energy performance without explaining the reasons for selecting specific types of window films. Additionally, the results are inconsistent between studies and demonstrate varying energy savings, primarily due to differences in conditions and types of window films used. In this context, the aim of this study is to develop a comprehensive decision-making framework that will assist in the selection of the most suitable window film sheet based on multiple criteria. This framework will provide a structured approach to evaluating and prioritizing key criteria for assessing window film sheets, ensuring that the most relevant factors are considered in the decision-making process. Through the integration of AHP and TOPSIS, decision-makers can prioritize criteria, weigh their importance, and ultimately make decisions that optimize the benefits of this solar technology. By utilizing AHP, the study aims to establish the relative importance of these criteria, allowing for a more informed and objective decision-making process. Additionally, the use of TOPSIS will enable the ranking and selection of the most suitable window film sheet based on the established criteria, further enhancing the decision-making process. In this study, eight criteria are considered to compare three window film sheets, namely, Silver 35, TrueVue 15, and Sterling 40, which are selected based on the UAE market.

2. Methodology

In this study, an integrated AHP-TOPSIS model is used to conduct multi-criteria decision-making (MCDM) to compare the three window films. This integrated model was mainly used due to the complementary strengths of both techniques (weighting and ranking calculations). AHP provides a structured framework for breaking down complex decisions into a hierarchy, facilitating pair-wise comparisons, and ensuring consistency in judgments; this helps in accurately capturing the decision-makers’ preferences. On the other hand, TOPSIS is efficient in ranking alternatives based on their relative closeness to an ideal solution, making it suitable for evaluating multiple options against various criteria. Figure 1 presents the methodology followed in this research, highlighting the use of AHP to calculate the weights of the criteria and TOPSIS for the evaluation of final scores.

2.1. AHP Weighting Model

The steps followed to calculate the weights of the criteria using the AHP method are presented below:

• Prepare the pair-wise comparison matrix: After selecting the criteria that will be used to compare the window films, a comparison between the selected criteria must be carried out. One effective comparison method is the pair-wise comparison matrix. This matrix allows for the comparison of all criteria against each other using a scale of 1 to 9. By evaluating each criterion in pairs, this method provides a structured and systematic approach to determining the relative importance of each criterion.
• Normalize the pair-wise comparison matrix: This involves adjusting the values in the matrix so that they are on a common scale to make them comparable. This is achieved by dividing each element of the matrix by the sum of the elements in its corresponding column, as shown in Equation (1). This process ensures that the sum of each column in the normalized matrix equals 1.

$$A_{norm}={\displaystyle \frac{A}{\sum _{i=1}^n{a}_i}}$$

(1)

where A is the original value in the pair-wise comparison matrix and a_i represents the elements in each column.

3.

Calculate the weights: After normalizing the pair-wise comparison matrix, the next step is to calculate the weights for each criterion. This is achieved by averaging the normalized values in each row of the matrix, which can be mathematically represented as

$$w_i={\displaystyle \frac{1}{n}}\sum _{j=1}^n{a}_{ij}$$

(2)

where w_i is the weight of the ith criterion, n is the total number of criteria, and a_ij represents the normalized value of the ith row and jth column.

4.

Check the consistency: Once the weights have been calculated, it is important to check the consistency of the pair-wise comparison matrix to ensure the reliability of the results. This involves calculating the consistency index (CI) and consistency ratio (CR), as shown in Equations (3) and (4), respectively.

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(3)

where λ_max is the largest eigenvalue of the matrix.

$$CR={\displaystyle \frac{CI}{RI}}$$

(4)

where RI is the random index, which is a predefined value that depends on the number of criteria. In the current study, where 8 criteria are taken into consideration, the RI is given as 1.41.

5.

Final weighting adjustments if necessary: If the consistency ratio exceeds the threshold, it suggests inconsistencies in the judgments, and the pair-wise comparisons should be reviewed and revised to improve consistency. The adjustments must be made without affecting the order of the criteria’s importance. After making the needed adjustments, the final weights must be calculated based on the average values of the different pair-wise comparison matrix samples.

Typically, a CR value of less than 0.1 is considered acceptable. According to the collected pair-wise matrices, the CR values were acceptable in most cases, with values less than the stated threshold. However, a few matrices slightly exceeded this acceptable range. Therefore, minor modifications were made to ensure that all CR values fell below 0.1, without affecting the order of the criteria. After these adjustments, the CR values ranged from 0.048 to 0.079.

2.2. TOPSIS

The TOPSIS ranking technique is a common method used to calculate the scores for different alternatives. It can be divided into five main steps as shown below:

• Normalization: The goal of normalization is to transform the data to a common scale to remove the effect of differences in the ranges of values. This makes the data dimensionless and comparable. Each element in the matrix (X_ij) can be normalized as follows:

$${\overline{X}}_{ij}={\displaystyle \frac{X_{ij}}{\sqrt{\sum _{i=1}^n{X^2}_{ij}}}}$$

(5)

where ${\overline{X}}_{ij}$ is the normalized value, X_ij is the element in the original matrix, i is the row number, and j is the column number.

• Weighting normalization: This step uses the normalized data from the previous step and the final average weights from the AHP model. A mathematical representation of this step is presented below:

$$V_{ij}={\overline{X}}_{ij}\times W_j$$

(6)

where V_ij is the weighted normalized value, ${\overline{X}}_{ij}$ is the normalized value, and W_j is the weight of the criterion.

• Ideal value calculation: This step involves evaluating the ideal best and ideal worst values based on the target of each criterion. For instance, if the criterion is considered as a positive factor, then the ideal best will be the maximum value, while if it is a negative factor, then the ideal best will be the minimum value and vice versa.
• Euclidean distances: This involves evaluating the distance of each alternative from the ideal and negative-ideal solutions. The corresponding procedures are shown in Equations (7) and (8), respectively.

$${S_i}^{+}=\sqrt{\sum _{j=1}^m{\left(V_{ij}-{V_j}^{+}\right)}^2}$$

(7)

$${S_i}^{-}=\sqrt{\sum _{j=1}^m{\left(V_{ij}-{V_j}^{-}\right)}^2}$$

(8)

where S_i⁺ and S_i⁻ represent the distances from the ideal best and ideal worst, respectively. V_ij is the weighted normalized value of the ith alternative for the jth criterion, V_j⁺ is the ideal value, and V_j⁻ is the worst value.

• Final score calculation: The final score for each alternative (P_i) is determined using Equation (9). This ratio indicates the relative closeness of each alternative to the ideal solution. A higher score value signifies that the alternative is closer to the ideal solution, making it more preferable.

$$P_i={\displaystyle \frac{{S_i}^{-}}{{S_i}^{+}+{S_i}^{-}}}$$

(9)

3. Multi-Criteria Decision-Making

The multi-criteria decision-making process considered in the current study compares three different solar window film sheet alternatives: Silver 35, TrueVue 15, and Sterling 40. These window films were chosen based on their commercial availability in the UAE market. They have proven to be effective in commercial offices, which is the focus of the current study. A detailed representation of the procedure followed in this research is demonstrated in Figure 2, showing the eight criteria taken into account, including visible light transmittance, solar energy rejected, energy transmittance, etc.

3.1. Criteria

When selecting window films for a commercial office, it is essential to consider various criteria to ensure choosing the most suitable option. Initially, twelve different criteria were listed to be included in the comparison. However, after collecting the data four of them were removed as they were almost consistent across all alternatives: UV protection, installation ease, environmental impact, and durability. By removing criteria that do not significantly impact the differentiation between window film alternatives, we can focus on the key factors that truly influence the performance and suitability of the films for commercial office space, making the decision-making process more streamlined and effective.

Choosing the right criteria for selecting window films is crucial for ensuring that you make an informed decision that aligns with the specific needs and goals. Each criterion plays a significant role in determining the performance, efficiency, and overall suitability of the window film for commercial office space. Table 1 provides a concise summary of why each criterion is essential when evaluating and selecting window films for commercial office spaces. Each criterion addresses specific aspects such as lighting, thermal performance, cost-effectiveness, occupant comfort, and maintenance, ensuring that the chosen window film solution meets the diverse needs of office environments effectively. Furthermore, detailed descriptions of the used criteria are provided below, highlighting the positive and negative factors:

• Visible light transmittance: This measures the amount of visible light that can pass through the window film, which influences natural lighting and productivity. Opting for a film with high visible light transmittance can help maintain natural lighting in your office space, which provides a well-lit and visually comfortable environment, promoting productivity and well-being among occupants. Higher visual light transmittance values allow more natural light to enter, which can reduce the need for artificial lighting, improve mood and productivity, and enhance occupant well-being. However, excessive transmittance can lead to glare issues, so balancing this criterion with glare reduction “C6” capabilities is important for optimizing visual comfort.
• Total solar energy rejected: The solar energy rejected by a window film indicates its ability to reduce heat transfer by radiation, and hence, the total amount of thermal energy entering the indoor environment. Selecting a window film with good thermal resistance can contribute to energy efficiency by reducing heat transfer, helping to regulate indoor temperatures, and potentially lowering cooling costs.
• Energy transmittance: This criterion assesses the amount of energy that can penetrate the film. As the transmittance increases, the cooling load also increases. Therefore, higher energy transmittance negatively affects the energy efficiency in the building and the costs associated with cooling.
• Energy absorptance: Energy absorptance measures the film’s capacity to absorb energy. Choosing a film with appropriate energy absorptance can impact the overall energy efficiency of your office. This criterion has a negative effect on the required performance of the window film since the absorbed energy will be released into the indoor environment. Thus, higher energy absorptance values can lead to increased cooling loads, thereby reducing the building’s energy efficiency.
• Cost: This is a crucial factor that influences the feasibility and adoption of window films in commercial office spaces. Balancing the upfront cost with long-term benefits and energy savings is essential for making a cost-effective decision. Considering the cost of window films is essential for budget planning and ensuring that the chosen option provides a good return on investment in terms of energy savings and performance. In this research, the data related to the cost were collected based on the UAE market as it is the case under study.
• Glare reduction: Glare reduction is important for creating a comfortable working environment by minimizing glare from direct sunlight on screens and surfaces. Opting for a film with glare reduction properties can enhance productivity and comfort for office occupants. Therefore, higher percentages of glare reduction can help improve visual comfort, enhance the usability of digital displays, and promote a conducive working environment by mitigating glare-related distractions.
• Visible light reflectance interior: This criterion evaluates the amount of visible light reflected from the interior side of the film. Controlling visible light reflectance can help manage lighting levels within the office, creating a pleasant and functional environment for work activities. Higher values of reflectance are not preferable since this will increase the effect of glare created by the window film.

Table 1. Definition of the different criteria considered in the current study.

Criterion	Description
Visible light transmittance	The percentage of visible light that passes through.
Total solar energy rejected	A measure of a material’s ability to resist the flow of heat by radiation.
Energy transmittance	The percentage of solar energy that passes through.
Energy absorptance	The percentage of solar energy that is absorbed.
Cost	The cost of the window film sheet.
Glare reduction	Reducing the unpleasant bright and strong sunlight.
Visible light reflectance interior	The percent of total visible light that is reflected by the window film/glass system.
Fade reduction	Relative reduction in the fading obtained by applying film.

Table 1. Definition of the different criteria considered in the current study.

Criterion	Description
Visible light transmittance	The percentage of visible light that passes through.
Total solar energy rejected	A measure of a material’s ability to resist the flow of heat by radiation.
Energy transmittance	The percentage of solar energy that passes through.
Energy absorptance	The percentage of solar energy that is absorbed.
Cost	The cost of the window film sheet.
Glare reduction	Reducing the unpleasant bright and strong sunlight.
Visible light reflectance interior	The percent of total visible light that is reflected by the window film/glass system.
Fade reduction	Relative reduction in the fading obtained by applying film.

Fade resistance: Fade resistance measures the film’s ability to protect interior furnishings, flooring, artwork, and materials from fading and degradation due to UV exposure. Selecting a fade-resistant film can help preserve the aesthetics and longevity of your office décor, reducing replacement and maintenance expenses.

3.2. Data Collection

Based on the collected data, the distinctions between window film sheets are evident in their performance characteristics (see

Table 2. The collected data for the investigated solar window film sheets.

Criteria		Alternatives
		Window Film Sheet 1—Silver 35 “A1” [19]	Window Film Sheet 2—TrueVue 15 “A2” [20]	Window Film Sheet 3—Sterling 40 “A3” [21]
Visible light transmittance	“C1”	35%	12%	41%
Total solar energy rejected	“C2”	63%	78%	63%
Energy transmittance	“C3”	26%	9%	28%
Energy absorptance	“C4”	41%	47%	34%
Cost (AED/5 m2) *	“C5”	120	140	145
Glare reduction	“C6”	61%	87%	54%
Visible light reflectance interior	“C7”	34%	20%	30%
Fade reduction	“C8”	67%	89%	61%

* AED: United Arab Emirates dirham.

). The data show that each type of film sheet (Silver 35, TrueVue 15, and Sterling 40) offers varying levels of the considered criteria. The data highlight how these factors can impact the amount of natural light entering a space, heat rejection, glare reduction, etc. Thus, these factors are important considerations when selecting the most suitable window film sheet for a commercial office space. As an initial analysis of the collected data, shown in Table 2, A2 has five superior characteristics compared to the other alternatives, such that it corresponds to the highest solar energy rejected, glare reduction, and fade reduction, as well as the lowest energy transmittance and visible light reflectance interior. On the other hand, it has a very low visible light transmittance, with a value of 12%, with the highest value corresponding to the Sterling 40 (41%), followed by Silver 35 (35%). Similarly, for the negative criterion “C4”, A2 has the worst value (47%). Even though A1 has only one best value, which corresponds to the cost criterion (120 AED), it is still considered competitive since it also has only one worst value (C7) and moderate values for all remaining criteria.

3.3. Scores and Ranking

Upon analyzing the results of the window film sheets based on the provided criteria and scores, it can be seen that Sterling 40 achieved the highest final score of 0.717497, securing the top rank. The detailed results are presented in

Table 3. The scores and ranking of window film sheets based on the TOPSIS technique.

Criteria	Silver 35	TrueVue 15	Sterling 40
C1	0.186309	0.063877	0.218248
C2	0.045405	0.056216	0.045405
C3	0.057975	0.020068	0.062434
C4	0.040318	0.046219	0.033435
C5	0.098558	0.114984	0.119091
C6	0.048278	0.068856	0.042738
C7	0.078093	0.045937	0.068906
C8	0.032956	0.043777	0.030004
Final Score *	0.65781	0.284454	0.717497
Rank	2	3	1

* The final score represents the relative closeness to the ideal solution and not the summation of criteria scores.

, showing the differences between the alternatives’ scores for all criteria as well as the final scores and ranking. It is very important at this point to note that the final scores are not the summation of the criteria scores since there are beneficial and non-beneficial factors. Moreover, the final scores represent the relative closeness of each alternative to the ideal solution, as illustrated in the methodology section. Although Sterling 40 holds the ideal scores for two criteria (C1 and C4), it performed the best overall among the three window film sheets evaluated. This is mainly attributed to the acceptable closeness of almost all its other criteria scores to the ideal solutions. Silver 35 follows closely behind with a final score of 0.65781, earning the second rank of two. While Silver 35 performed well, it fell slightly short compared to Sterling 40 in terms of the specified criteria. TrueVue 15, with a final score of 0.284454, obtained the third rank of three. TrueVue 15 had the lowest performance among the three window film sheets based on the evaluation criteria. Although it had five superior characteristics, as mentioned previously, its very low visible light transmittance decreased its score considerably, especially due to the high weight of this criterion, which resulted in a huge difference between its score and the ideal solution. In order to better understand and visualize the difference between the alternatives’ scores, the normalized scores were calculated, as depicted in Figure 3. The normalized score does not affect the ranking of the investigated alternatives, it is only used to make the summation of all scores equal to 1 for a better representation and analysis. Therefore, the normalized score of each alternative was calculated by dividing the final score of the corresponding alternative by the summation of the final scores of the three window films.

Based on the proposed model, Sterling 40 is the top performer overall, but Silver 35 could be a more cost-effective option depending on individual needs. TrueVue 15, while ranking lowest in this evaluation, may still have specific features or benefits that make it a suitable choice for certain applications. In other words, these scores and rankings are highly affected by the criteria weights as they may differ significantly from one application to another. Due to these reasons, a comparison between the proposed AHP-TOPSIS and No Priority models is presented in

Table 4. A comparison between the proposed AHP-TOPSIS and No Priority models.

Method	AHP-TOPSIS		No Priority
Window Film	Score	Rank	Score	Rank
Silver 35	0.396	2	0.294	3
TrueVue 15	0.171	3	0.382	1
Sterling 40	0.432	1	0.324	2

. No Priority is a method that gives equal weights for all criteria, which means that in this case the weight of each is assumed to be 0.125. In this table, the normalized scores are taken into account to make the numbers comparable between the two models. It can be noticed from these results that there is a huge difference between the scores and rankings, such that in the No Priority model, TrueVue 15 is ranked first. This confirms the importance of this study as it considers a specific market “UAE”, where the weights of the criteria may be different and affected by the available local conditions.

As shown in Table 4, the criteria weights have a huge impact on the ranking of window films, and consequently, this affects the decision of selection. In order to further investigate and visualize the effect of criteria weights, a sensitivity analysis was conducted, as depicted in Figure 4. In each of the displayed figures, the weight of one criterion was varied between 0 and 0.9, while the weights of the other criteria were distributed equally. It can be noticed that varying the criteria weights affects the ranking of the solar window film alternatives except in the case of varying C2 and C7. In both cases, the ranking of the alternatives was in the following order regardless of the criterion weight: TrueVue 15, Sterling 40, Silver 35. Furthermore, C6 and C8 have an almost equal effect on the alternatives scores and rankings, such that TrueVue 15 performed the best in both, while Silver 35 and Sterling 40 switched their rankings around a criteria weight of approximately 0.3.

4. Conclusions

When it comes to selecting a window film sheet, employing a multi-criteria decision-making approach can be highly beneficial in ensuring that the chosen option aligns with the specific requirements and priorities of the user. This method involves evaluating and comparing different window film sheets based on multiple criteria or factors, rather than solely focusing on a single aspect. It is essential to consider not only the performance ranking but also other factors such as cost, specific requirements, and preferences when selecting the most suitable window film sheet. In the current study, the evaluation of the window film sheets based on the specified criteria and scores has provided valuable insights into their performance, allowing ranking of three different window films, namely, Silver 35, TrueVue 15, and Sterling 40. Based on the proposed AHP-TOPSIS model, Sterling 40 emerged as the top performer with the highest final score, showcasing its superior performance across the evaluated criteria. Silver 35 followed closely behind, securing the second rank with a commendable final score. TrueVue 15, while ranking third in this evaluation, may still offer unique features or benefits that could be advantageous in certain applications. The normalized scores of the compared window film sheets were 0.43, 0.40, and 0.17 for Sterling 40, Silver 35, and TrueVue 15, respectively. Since the criteria weights were influenced by the current status of the UAE market, which was taken as a case study, a comparison between the AHP-TOPSIS and No Priority techniques was provided. The resulting rankings were inconsistent between the two approaches, confirming the need for applying MCDM in each specific case based on the available data and conditions.

Even though the proposed AHP-TOPSIS model has proven to be an effective tool for MCDM procedures, both methods have limitations. For example, AHP can be subjective, and TOPSIS may be sensitive to the scaling of data. These assumptions and limitations could impact the reliability of the results if not carefully managed. Furthermore, even when considering the same criteria, the scores and ranking may differ from one application to another due to the possible critical changes in criteria weights. From this perspective, it would be beneficial to comprehensively investigate the relation between criteria weights and different applications and how this may affect the scores and ranking of solar window film sheet alternatives.

Balancing the initial affordability of window films with their long-term economic benefits is crucial for maximizing energy efficiency and operational savings in commercial office spaces. Various criteria related to the cost-effectiveness of window films affect the building’s energy consumption, such as energy transmittance and solar energy rejection. Additionally, other criteria indirectly impact long-term costs, including fade reduction, which can decrease maintenance costs and the need for replacing interior furniture and materials. From this perspective, future research could investigate novel financing models or incentives that encourage the adoption of energy-efficient window films in commercial office buildings, considering both initial investments and long-term savings.