Integrating Personalized Thermal Comfort Devices for Energy-Efficient and Occupant-Centric Buildings

Abstract

Personalized thermal comfort (PTC) systems aim to satisfy the individual thermal preferences of occupants rather than relying on average comfort indices. With the growing emphasis on sustainability and reducing energy consumption in buildings, energy efficiency has become a critical factor in the design and selection of PTC systems. While the development of PTC tools has accelerated in the last decade, selecting the most appropriate system remains a challenge due to the dynamic, uncertain, and multi-dimensional nature of the decision-making process. This study introduces a novel application of the KEMIRA-M multi-criteria decision-making (MCDM) method to identify the optimal PTC system for university office buildings—an area with limited prior investigation. A case study is conducted in a naturally ventilated office space located in a temperate climate zone. Eight distinct PTC alternatives are evaluated, including data-driven HVAC systems, wearable devices, and localized conditioning units. Six key criteria are considered: estimated energy consumption, capital cost, indoor and outdoor space requirements, system complexity, mobility, and energy efficiency. The results indicate that wearable wristbands, which condition the occupant’s carpus area, offer the most balanced performance across criteria, while radiant ceiling/floor systems perform the poorest. Energy efficiency plays a crucial role in this evaluation, as it directly impacts both the operational cost and the environmental footprint of the system. The study’s findings provide a structured and adaptable framework for HVAC engineers and designers to integrate PTC systems into occupant-centric and energy-efficient building designs.

1. Introduction

The aim of Heating, Ventilating, and Air Conditioning (HVAC) systems is to satisfy thermal comfort for occupants [1]. Traditional HVAC systems regulate air temperature and fan speed in order to control thermal environments [2]. However, thermal comfort varies for every person due to the personal parameters such as gender, culture, weight, and race [3,4]. On the other hand, personalized thermal comfort (PTC) takes every individual thermal comfort into account instead of taking the average thermal sensation of a large group, i.e., the Predicted Mean Vote and Predicted Percentage of Dissatisfied (PMV/PPD) method [5]. Occupants prefer to use PTC tools such as a desk fan, developed heated/cooled chairs, and radiant heating/cooling systems instead of using traditional HVAC systems. These tools allow individual control of thermal comfort for every occupant. For instance, Turhan et al. [6] developed a PTC controller that uses several environmental sensors such as a Passive Infrared Sensor (PIR), indoor air temperature (Ti), and relative humidity (RH) sensors. Furthermore, the controller learns the thermal preferences of each occupant by using a fuzzy logic (FL) approach and operates the HVAC system according to the desired indoor conditions. The authors indicated that the developed PTC controller satisfied thermal comfort in 88% of the total measurement days while the traditional HVAC system satisfied in only 4% of the measurement days. Watanebe et al. [7] developed a heated/cooled chair by using heating strips and cooling fans. The authors concluded that the PTC of each occupant was satisfied even when the ambient temperature was 30 °C. Brooks et al. [8] used a PTC controller with environmental sensors such as a Passive Infrared Sensor (PIR) to understand the existence of the occupancy, to measure the temperature and relative humidity with environmental sensors, and to measure indoor air quality (IAQ) with a carbon dioxide sensor in order to satisfy indoor air quality. The results showed that the developed PTC controller saved 37% of energy compared to traditional HVAC systems. On the other hand, Jazizadeh et al. [9] implemented one of the Artificial Intelligence (AI) methods, fuzzy logic (FL), to learn the PTC profiles of the occupants. The occupants voted for their thermal preferences while objective sensors measured the environmental conditions. Afterwards, the PTC controller regulated the HVAC system according to the occupant’s thermal preferences. The authors concluded that the novel PTC controller saved 39% of daily average air flow rate compared to the traditional HVAC system. Recent studies showed that PTC tools are very common after the early 2000s; however, the price of the sensors is still very high [10]. Many PTC tools are hard-to-use for occupants, because of their complicated structures. Regarding this aim, the selection of the optimal air conditioner system to obtain the PTC for each individual occupant becomes a vital task.

Multi-criteria decision-making (MCDM) is a general term for any approach that supports people to take decisions in cases with many contradictory criteria [11]. The MCDM methods are categorized as follows: Elementary Methods, Single Synthesizing Criterion, Out-Ranking Methods, and Mixed Methods [12]. Several of the MCDM approaches such as the Analytic Hierarchy Process (AHP), Choosing by Advantages (CBA), the Preference Ranking Organization Method For Enrichment Evaluations (PROMETHEE), and the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) are applied in HVAC system selections for any building and/or climate zone [13,14,15,16].

In the AHP, a multiple criteria problem is structured hierarchically by breaking down a problem into smaller and smaller consistent parts [17]. The goal (objective) is at the top of the hierarchy, criteria and sub criteria are at levels and sub-levels of the hierarchy, respectively, and decision alternatives are at the bottom of the hierarchy. The best alternative is usually selected by making comparisons between alternatives with respect to each attribute [16,17]. The AHP has been used in the selection of HVAC systems in [18,19,20].

The CBA is a special form of the MCDM approach that is focused upon separating the alternatives by summarizing their advantages and by weighting the value of advantages in the subjective portion of the decision at the culmination of the procedure [21]. Moreover, the CBA attempts to find consensus among decision-makers in the context of the decision and offers alternatives [22]. The CBA has been used in the selection of HVAC systems in [23,24].

The PROMETHEE approach uses the outright theory to identify options with ease of use and reduced complexity. It contrasts alternatives in a couple of ways so that they are categorized according to a range of parameters [25]. The PROMETHEE has been used in the selection of HVAC systems in [26].

The TOPSIS is one of the commonly used MCDM methods in HVAC system selections. The TOPSIS is based on the concept that the best alternative should have the shortest distance from the ideal solution. The TOPSIS has been used in the selection of HVAC systems in [27,28,29].

Table 1. Applied MCDM methods to proper HVAC system selection.

MCDM Method	References
AHP	[18,19,20]
CBA	[23,24]
PROMETHEE	[26]
TOPSIS	[27,28,29]

depicts a summary of the applied MCDM methods for the selection of the optimal HVAC systems.

The field of HVAC systems plays a pivotal role in ensuring energy efficiency, thermal comfort, and indoor air quality (IAQ) across various building types, yet there remains a lack of comprehensive discussion on some critical issues. For instance, ventilation strategies in near-zero- or zero-energy buildings present unique challenges in balancing energy conservation with the need for fresh air, with methods such as CFD simulations helping to assess CO₂ transport in passive houses. Furthermore, heating system choices must be regionally tailored, with dynamic real-time measurements comparing systems like gas and wood furnaces in dual-fuel setups to evaluate occupant safety and indoor air quality. Additionally, a significant but often overlooked topic is the accumulation of contaminants within HVAC systems, such as radon, which can be influenced by human activity and pose risks to IAQ, especially in small office spaces. Addressing these issues is vital for developing HVAC solutions that prioritize both environmental sustainability and the health and well-being of building occupants.

In recent years, there has been a growing interest in personalized thermal comfort (PTC) systems, which aim to meet the individual thermal preferences of occupants rather than relying on average comfort indices. Several studies have explored various PTC solutions, including data-driven HVAC systems, wearable devices, and localized conditioning units, with a focus on improving occupant comfort while enhancing energy efficiency. However, much of the existing research is limited in scope, often focusing on specific device types or climate conditions. For example, while several studies have evaluated the performance of wearable devices for thermal comfort, limited attention has been given to the integration of these devices with HVAC systems or to evaluating their effectiveness in different building types and climate zones. Furthermore, most of the existing research does not provide comprehensive multi-criteria evaluations that consider a range of factors such as energy consumption, cost, system complexity, and mobility, which are crucial for real-world implementation. This study addresses these gaps by applying the KEMIRA-M multi-criteria decision-making (MCDM) method to identify the optimal PTC system for university office buildings, an area that has received limited investigation. The novelty of this research lies in its structured approach to evaluating multiple PTC alternatives across several performance criteria, offering a more comprehensive and adaptable framework for HVAC engineers and designers. In addition to the growing body of research on the performance and benefits of personalized thermal comfort (PTC) systems, several studies have highlighted the practical challenges associated with their implementation in real-world environments. These challenges include the high initial capital costs of advanced PTC technologies, the operational complexity of integrating such systems with existing HVAC infrastructure, and the lack of standardization in controls and communication protocols. Moreover, PTC systems often require user adaptation and can pose difficulties in shared or open-plan office layouts where thermal preferences may conflict. Integration constraints—such as building geometry, ventilation design, and retrofit limitations—further complicate deployment. Therefore, when evaluating the suitability of PTC alternatives, it is essential to consider not only performance metrics but also cost, scalability, and compatibility factors. This study incorporates these considerations by applying a multi-criteria decision-making approach that accounts for both the technical and practical dimensions of system implementation.

The primary contribution of this paper lies in applying the KEMIRA-M method specifically to university office buildings, which, as per our literature review, has not been extensively explored in the context of office buildings. Therefore, in this paper, the Kemeny Median Indicator Rank Accordance-Modified (KEMIRA-M) method is used in the selection of the optimal air conditioning system to obtain personalized thermal comfort in university office buildings. The advantage of this method is the ability to solve real-life multi-alternative problems based on various criteria [30].

2. Materials and Methods

The method of the study consists of gathering information on the PTC systems from the literature, applying these systems for a real case building in a temperate zone, and using the KEMIRA-M method for the decision-making process.

2.1. Case Building

The case building was selected as a university office building located in Ankara/Republic of Turkey (39.81° N 32.72° E). The case building dimensions are 4.7 m in depth, 3.25 m in width, and 2.8 m in height. Moreover, the case building has one large window (window to wall ratio: 3.6), which faces in the south direction. The location of the case building is depicted in Figure 1.

The city of Ankara is classified as a Csb type climate zone, which is used to represent the warm summer Mediterranean climate, by the Köppen–Geiger climate classification [31]. The averages of the minimum and maximum temperature of the city of Ankara are 7.3 °C and 18.4 °C, respectively, during a year [32]. The office building is naturally ventilated and there is no heating/cooling system in the building. Therefore, the occupants prefer to use personalized air conditioner systems.

The selection of a university office building as the case study is intentional, given its relevance and representativeness within the broader category of shared, small-to-medium-sized workspaces. These buildings often operate under budget constraints, have aging HVAC infrastructure, and accommodate diverse occupant schedules—all factors that highlight the growing need for adaptable and energy-efficient solutions like PTC systems. Moreover, university offices reflect many characteristics of typical public-sector and institutional buildings, making the findings transferable to similar contexts. As for the system performance outcomes, radiant heated/cooled ceiling and floor systems rank lower primarily due to their high initial installation costs, low flexibility, and extensive spatial requirements. Unlike portable or wearable alternatives, these systems are static and demand invasive structural modifications, limiting their feasibility in retrofitted or space-constrained buildings. This performance gap underscores the importance of not only evaluating energy and thermal performance but also considering practical deployment constraints in real-world applications.

2.2. Selected PTC Systems for Office Buildings

Eight different PTC systems were selected for the case office building.

Table 2. The selected PTC systems and their information for office buildings.

Device	Ref.	Power (W)	Capital Cost * (USD)	Indoor Area (m²)	Outdoor Area (m²)
PTC-DC (A1)	[6]	7.4	$69	0.0221	NA
Wristband (A2)	[33]	1.54	$90	0.0016	NA
Desk Fan (A3)	[34]	2000	$52	0.0322	NA
Heated/Cooled Chair (A4)	[35]	16.5	$85	0.3252	NA
Air Conditioner (A5)	[36]	8000	$535	0.2578	0.3463
Radiator (A6)	[37]	2000	$75	0.1249	NA
Radiant Ceiling/Floor (A7)	[38]	500	$855	1.5	10
Infrared Glasses (A8)	[39]	1.1	$70	0.09	NA

* Capital cost includes equipment, sensors, and installation costs.

depicts the selected PTC systems and their features in detail. The PTC systems were selected from the literature with a detailed investigation. The reasons for selecting these systems for this study were their easy implementation and applicability to the university office buildings. The energy consumption, cost, and indoor and outdoor requirements of the systems were taken from the articles in the literature. If this information was not available in the article, it was calculated according to the number of used sensors and their features. It is worth noting that the authors prefer to use “Estimated” for energy consumption and capital cost parameters since some of these values were calculated values. Moreover, “NA” in Table 2 represents there is no outdoor area requirement for the corresponding PTC system. Another important reminder is for the PTC-DC and infrared sensing system devices. These devices require an integrated HVAC system for operation. Therefore, this limitation is added to the “Complexity” factor, which will be discussed in Section 2.3.

2.3. KEMIRA-M Method and Criterion

The KEMIRA-M method is a multi-criteria decision-making tool used to solve complex optimization problems by evaluating multiple factors and criteria. It integrates both quantitative data and qualitative assessments to identify the best solutions across various domains, such as engineering, environmental science, and management. The method uses a systematic approach to process different variables, such as performance, cost, and environmental impact, and applies weighting factors to these variables based on their relative importance. KEMIRA-M generates an optimal or near-optimal solution by analyzing different alternatives and providing recommendations based on the specific objectives of the study. Its flexibility allows for application in diverse fields, making it a valuable tool for addressing decision-making challenges where multiple, sometimes conflicting, factors need to be considered simultaneously. The method is particularly useful in scenarios where there is a need for the precise analysis of different options to achieve a balance between efficiency, cost, and other relevant outcomes.

This study uses the KEMIRA-M method for selecting the optimal PTC system for a university office building. The method uses expert reports and much less information on the systems compared to other multi-decision-making tools. Eight alternatives, which are discussed in Table 2, were selected for the KEMIRA-M calculations.

Table 3. The factors used in the KEMIRA-M method.

Main Factors	Unit	Source
Estimated Energy Consumption (X1)	W	Calculated/taken from articles in Table 2
Estimated Capital Cost (X2)	$	Calculated/taken from articles in Table 2
Indoor Area Requirement (X3)	m2	Calculated/taken from articles in Table 2
Outdoor Area Requirement (X4)	m2	Calculated/taken from articles in Table 2
Auxiliary factors
Complexity (Y1)	-	Expert
Mobility (Y2)	-	Expert

represents the factors that affect the selection of the optimal PTC for a university office building. Three experts, who live in temperate climate zones, were selected from the fields of energy and thermal comfort. Auxiliary factors were evaluated with face-to-face interviews with non-quantitative evaluations of the experts. Adequacy of each factor was controlled and verified by the experts and these factors were evaluated as vital factors to select the most suitable PTC system for an office building.

Estimated Energy Consumption (X1) is the amount of energy consumed by the PTC system in Watt units. Moreover, Estimated Capital Cost (X2) represents the investment cost of the system related to all equipment such as sensors, frames, and installment costs. Indoor Area Requirement (X3) is related to the total area of the PTC system in an office building while Outdoor Area Requirement (X4) depicts the needed outdoor space of the building. These four factors were selected as the main factors that affect the selection. On the other hand, two factors called complexity and mobility of the PTC system were selected as auxiliary factors in the decision-making process. Complexity (Y1) is the number of components inside the PTC system. If the system has many components, the complexity of the system increases the risk of failures, difficulties in finding the components, and high maintenance costs. The last criteria is Mobility (Y2), which means the ability to transport the system to another office building.

Mobility and complexity play critical roles in the selection of PTC systems, as both directly influence user experience and overall occupant comfort. Mobility refers to the ease with which a system can be relocated or adjusted by the occupant, which is particularly important in dynamic office environments where users may change locations or preferences frequently. Highly mobile systems, such as wearable devices or desk fans, offer greater flexibility and user control, contributing to improved thermal satisfaction. On the other hand, system complexity encompasses the level of technical difficulty involved in installing, operating, and maintaining a PTC device. Systems with high complexity may deter user engagement, increase operational errors, or require specialized knowledge for effective use, ultimately reducing their practicality. In this study, both factors were included as decision criteria to reflect their real-world significance. Devices that balanced low complexity with high mobility—such as wristbands—tended to score more favorably, suggesting that intuitive, user-friendly solutions may have higher acceptance and comfort outcomes in everyday office settings.

The important steps of the KEMIRA-M calculations were followed in the study as below:

(a)

All criterion and alternatives were defined for the calculation.

(b)

Construct the initial decision matrix (DM) by Equation (1).

$$\mathrm{DM}=\left(\begin{array}{ccc}{X}_1^1& \cdots & X_4^1\\ ⋮& \ddots & ⋮\\ X_1^3& \cdots & X_1^3\end{array}\right)\left(\begin{array}{ccc}{Y}_1^1& \cdots & Y_2^1\\ ⋮& \ddots & ⋮\\ Y_1^3& \cdots & Y_1^3\end{array}\right)$$

(1)

here, the first matrix shows the decision matrix for the main factors while the second is the decision matrix for the auxiliary factors.

(c)

Normalize the initial decision matrix with a range of [0, 1]. The normalization was processed by subtracting the minimum value from the corresponding value and dividing it into maximum and minimum value differences.

(d)

Construct the correlation matrix for the factors.

(e)

Determine the median priorities of the main (X) and auxiliary (Y) factors via obtaining one or a few medians.

(f)

Rank the alternatives.

Further details for the calculation of KEMIRA-M for this study can be found in [30,40].

The assignment of weights for the decision-making criteria such as energy consumption, cost, space requirements, system complexity, and mobility is based on expert input gathered through structured interviews with three HVAC professionals experienced in building systems and thermal comfort technologies. Each expert was asked to independently rank the importance of the six criteria, and the final weights were determined by averaging these rankings using the KEMIRA-M prioritization framework. While this method provides a transparent and expert-informed weighting scheme, it is acknowledged that differences in expert opinions may influence the results. To evaluate the robustness of the final ranking, a sensitivity analysis will be included in future studies, testing how variations in criteria weights affect the selection outcome. Incorporating this step would help assess the reliability of the decision model and improve its applicability across different use cases.

3. Results

To provide better solutions for decision-makers in order to select the optimal PTC system for a university office building, the KEMIRA-M method was used for the study. Since the problem here is an optimization problem, optimization functions were expected as follows. Estimated Energy Consumption (X1) and Estimated Capital Cost (X2), Indoor and Outdoor Area Requirements (X3 and X4) needed to be the lowest one. On the other hand, Complexity (Y1) needed to be the lowest while Mobility (Y2) needed to be the highest one.

Table 4. Significance of all the factors evaluated by the experts.

Expert Person ID	X1	X2	X3	X4	Y1	Y2
A	3	1	2	4	1	2
B	1	3	2	4	1	2
C	2	1	3	4	2	1

depicts the significance of the factors evaluated by the experts.

In Table 4, Expert A indicated that Estimated Capital Cost (X2) is the most influencing factor for a PTC system in an office building while Outdoor Area Requirement (X4) is the lowest one among the main factors. On the other hand, Complexity (Y1) is more vital than Mobility (Y2) for Expert A.

The discrepancies in priority assignments for “Cost (X2)” and “Complexity (Y1)” across the different experts, as presented in Table 4, are understandable given the subjective nature of expert judgment. Each expert brings a unique perspective based on their expertise, experience, and understanding of the specific context in which the personalized thermal comfort (PTC) systems will be applied. For example, Expert A may prioritize cost due to concerns about budget constraints in practical implementation, while Expert C may place a higher value on complexity to ensure that the system is easy to operate and maintain. To reconcile these differences, this study employed a consensus-building approach, considering the weightings provided by all experts and averaging their rankings. This allows for a balanced approach that accounts for the diversity of expert opinions while reflecting the collective judgment.

Table 5. DM results.

Alternatives	X1	X2	X3	X4	Y1	Y2
A1	7.4	69	0.0221	0.001	2	3
A2	1.54	90	0.0016	0.001	1	2
A3	2000	52	0.0322	0.001	8	4
A4	16.5	85	0.3252	0.001	4	5
A5	8000	535	0.2578	0.03463	7	8
A6	2000	75	0.1249	0.001	6	6
A7	500	855	1.5	10	5	7
A8	1.1	70	0.09	0.001	3	1

and

Table 6. Normalized matrix results.

Alternatives	X1	X2	X3	X4	Y1	Y2
A1	0.000787	0.02117	0.01301	0	0.14285	0.28571
A2	0.00005	0.04732	0	0	0	0.14285
A3	0.24989	0	0.02042	0	1	0.42857
A4	0.001925	0.00412	0.21596	0	0.42857	0.57142
A5	1	0.60149	0.17132	0.00336	0.85714	1
A6	0.24989	0.02864	0.08228	0	0.71428	0.71428
A7	0.06237	1	1	1	0.57142	0.85714
A8	0	0.02241	0.05899	0	0.28571	0

show the initial DM and normalized matrices, respectively. It is worth noting that the priority of criteria is determined by the Kemeny Median method and the final ranking of the PTC systems is shown in

Table 7. Final ranking of alternatives for setting the optimal solution.

Alternatives	Sum of the Weighted Averages	Rank
A1	1.0589	3
A2	1.6178	1
A3	0.9875	4
A4	0.6145	5
A5	0.2597	7
A6	0.4132	6
A7	0.1698	8
A8	1.1489	2

.

Table 7 represents the final ranking of the alternatives. The larger value of the sum of weighted averages means the best alternative for the selection. The result gives A2 > A8 > A1 > A3 > A4 > A6 > A5 > A7 according to the Kemeny Median method. The larger sum of the weighted averages is A2 with a value of 1.61. Therefore, the optimal PTC system solution for the office buildings is using the wristband. The wristbands require less indoor area while no outdoor area is required. Moreover, the energy consumption of them is lower compared to other alternatives. Another important reason for selecting these systems as the optimal solution could be their mobility and easy-to-use concept. The occupants can adopt these PTC systems for every office building easily. Nonetheless, wrist-worn systems could be difficult while performing regular activities like working on a computer in an office environment [41].

The second optimal solution is calculated to be an infrared sensing system by wearing eyeglasses. However, wearing these eyeglasses in a working environment could be difficult. The worst choice is found to be radiant heating/cooling systems because of their high installation cost and low mobility.

In the KEMIRA-M method applied in this study, six decision criteria were evaluated based on expert input to reflect the multi-dimensional aspects of selecting a personalized thermal comfort (PTC) system. As shown in Figure 2, the most influential factor was Estimated Capital Cost (X2), followed closely by Estimated Energy Consumption (X1) and Indoor Area Requirement (X3). These findings emphasize that both financial and spatial constraints are central to decision-making in office buildings. Auxiliary factors such as Complexity (Y1) and Mobility (Y2) also held significant weight, underlining the importance of system usability and adaptability in real-world conditions. Notably, Outdoor Area Requirement (X4) received the lowest weight, which is understandable given that many PTC systems are designed for indoor use without requiring exterior installations.

This distribution reveals a clear priority trend: stakeholders prefer cost-effective, energy-efficient, and compact solutions that are easy to operate and relocate. These preferences align well with current trends in sustainable and occupant-centric building design. When comparing this prioritization with the literature, similar emphasis on cost and usability can be found in HVAC selection studies using AHP or TOPSIS. However, KEMIRA-M’s ability to integrate auxiliary factors like mobility and complexity offers a nuanced view, which may explain the wristband’s superior performance in this study. Future studies could expand on this by including additional criteria such as noise level or user satisfaction, depending on the specific application context.

When compared with similar studies in the literature, these findings are consistent with existing trends. For instance, Baç et al. [16] and Elkhayat et al. [17], who used the AHP and hybrid simulation-based MCDM approaches, also identified cost and energy performance as the most influential criteria in HVAC system selection. Similarly, Wong and Li [18], applying the AHP for intelligent building system evaluation, emphasized ease of use and investment cost as major determinants. However, unlike many traditional methods, the KEMIRA-M framework employed in this study also integrates user-centric factors such as mobility and complexity, which are often overlooked in other decision models. This integration allows a more holistic evaluation of PTC alternatives, particularly valuable in dynamic work environments like shared offices. Thus, KEMIRA-M offers a unique advantage by balancing quantitative performance indicators with qualitative usability considerations, which may explain why wearable solutions such as wristbands ranked highest in this study—despite their low technical complexity and minimal spatial demands.

The KEMIRA-M method was chosen for this study due to its ability to effectively handle complex, multi-dimensional decision-making problems involving both qualitative and quantitative criteria, which are characteristic of personalized thermal comfort (PTC) system evaluations. While other well-known multi-criteria decision-making (MCDM) methods, such as the AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), are widely used, they each have their limitations in the context of this study. The AHP, for example, requires pairwise comparisons between criteria, which can become time-consuming and prone to inconsistencies as the number of criteria increases, making it less efficient when dealing with numerous factors. On the other hand, the TOPSIS is based on the concept of ideal and negative-ideal solutions, which can sometimes overlook the nuances of criteria interactions and the importance of balancing trade-offs between them. In contrast, KEMIRA-M offers a more straightforward approach that simplifies the decision-making process while still accounting for the complexity of real-world systems. Its flexibility in integrating diverse criteria and handling uncertainties makes it particularly suited for this study, where multiple PTC alternatives need to be evaluated across various performance metrics. Unlike the AHP and TOPSIS, KEMIRA-M also allows for a more intuitive understanding of how different factors contribute to the final decision, making it an advantageous choice for this particular case study.

4. Discussion

This study has some limitations. Firstly, the case office building is a small and single-user office building. Selecting a PTC system for a multi-occupant office building is always a difficult task. In this study the KEMIRA-M method is used for selecting the optimal PTC system solution for office buildings. The reason for selecting this method is its simplicity with fewer criterion and data. However, other MCDM methods such as SWARA and WASPAS could be used for the selection. Furthermore, this study is limited by its focus on a single office space, which may not be representative of the performance of PTC systems in other office environments with varying conditions and characteristics.

Secondly, PTC systems generally use different machine learning algorithms. For instance, the PTC-DC [6] uses a fuzzy logic algorithm to learn occupants’ thermal preferences. This study considers these systems fully trained and accurate for predicting occupants’ thermal preferences. Lastly, the selected experts for this study are located in temperate climate zones according to the Köppen–Geiger climate classification map. Climate adaptation can affect expert decisions. Moreover, psychological aspects such as the existence of a PTC system in an office environment can affect the thermal sensation of the occupants. For instance, “Turhan and Özbey coefficients” [42] add a mood state correction factor over a traditional PMV method while calculating the actual thermal sensation. Therefore, other criterion such as the aesthetic and noise of the PTC system, which affects the occupant’s psychology, could be taken into account when selecting the optimal solution.

This study does not consider other key performance indicators such as “user satisfaction” and “response time for thermal adjustment”, which are essential for evaluating the effectiveness of personalized thermal comfort (PTC) systems. Future research should include these critical metrics to provide a more comprehensive assessment of PTC system performance and their impact on occupant comfort and responsiveness.

It is important to note that all the experts involved in this study are from temperate zones, and as such, the performance of the evaluated PTC systems, such as desk fans (A3), may vary significantly in other climates, particularly in humid tropical regions. Future research should incorporate climate-specific factors, including temperature, humidity, and local weather conditions, to assess the adaptability and effectiveness of these systems in different environmental settings, ensuring that the results are applicable across diverse climatic zones.

In addition to physical comfort, psychological factors such as mood and overall well-being have a profound impact on an occupant’s perception of thermal comfort. For instance, Turhan and Özbey coefficients depict even mood state affects the thermal comfort of occupants. Therefore, research into the psychological effects of having a PTC system in the office, including its influence on job satisfaction and productivity, could provide a more comprehensive understanding of the benefits of these systems. Furthermore, aesthetic considerations and the noise generated by these systems should be factored into the decision-making process, as they can affect both the physical comfort and mental well-being of users.

Climate plays a significant role in determining the effectiveness of PTC systems. The current study primarily focuses on temperate climates, but the performance of these systems can vary significantly in different regions, particularly in humid tropical or extreme cold climates. Future studies should incorporate climate-specific factors such as temperature, humidity, and seasonal weather patterns to evaluate how PTC systems perform across diverse environmental conditions. This would ensure that the findings are applicable to a broader range of geographic locations.

Lastly, one can call that noise is a critical criterion in HVAC and personalized thermal comfort (PTC) system evaluations, particularly in environments like office buildings where acoustic comfort directly affects productivity and occupant satisfaction. In this study, noise was initially considered during the criteria selection phase. However, it was excluded from the final evaluation for two main reasons. Firstly, reliable and comparable noise level data (in decibels, dB) for all eight PTC alternatives—especially wearable and compact systems—were not consistently available across sources. Including these incomplete data risked introducing bias or misinterpretation into the multi-criteria decision-making process. Secondly, based on expert consultations, the primary focus of the case study was placed on energy efficiency, cost, space, and system usability, which were considered more pressing in the context of the naturally ventilated office setting analyzed.

5. Conclusions

Personalized thermal comfort (PTC) systems have emerged as a promising solution to address the limitations of traditional HVAC systems, which often fail to meet the diverse comfort needs of occupants in office buildings. However, selecting the most suitable PTC system remains a complex challenge, largely due to the non-linear nature of thermal comfort, where the relative importance of various input parameters remains unclear. Additionally, the expense and complexity of these systems further complicate the decision-making process. To make informed and effective choices, multi-criteria decision-making (MCDM) tools are essential. In this study, the KEMIRA-M method was employed, a simplified and user-friendly approach, to evaluate and select the optimal PTC system for an office building.

The study considered six distinct criteria to evaluate eight different PTC systems, with the results highlighting wristband-based systems as the optimal solution, according to the selected criteria. However, this study is not without its limitations, particularly in terms of the scope and external validity of the results. Future research should incorporate additional influencing factors, such as psychological influences, occupant preferences, and environmental variability, which could provide a more comprehensive understanding of how these systems perform in a variety of settings. Expanding studies to include a broader range of office types and environmental conditions would also help clarify the effectiveness and optimization of PTC systems across diverse office environments.

The practical implications of this study are significant for HVAC engineers and building designers who are seeking to integrate occupant-centric thermal comfort solutions. The decision-making framework based on the KEMIRA-M method provides a structured approach for evaluating multiple, often conflicting criteria such as energy consumption, cost, mobility, and system complexity. This systematic evaluation process supports the selection of the most appropriate PTC system in various building contexts, especially in retrofitting scenarios where space and budget constraints are a priority. Additionally, the study emphasizes the importance of integrating user-centric technologies early in the design process, fostering the shift toward more adaptive, energy-efficient, and personalized indoor environments.

Further research should incorporate real-world pilot studies, long-term user feedback, and climate-specific considerations to assess the real-world performance and adaptability of PTC systems. Incorporating these factors will enhance the understanding of these systems’ effectiveness in diverse settings, ensuring that PTC solutions can be optimized for both occupant comfort and sustainability in a wide range of office environments.