Research on the Design Strategy of Double–Skin Facade in Cold and Frigid Regions—Using Xinjiang Public Buildings as an Example

Abstract

In the context of global warming, the focus on applying and researching double–skin facade (DSF) systems to reduce energy consumption in buildings has significantly increased. However, researchers have not thoroughly examined the performance and applicability of DSFs in severe cold regions with high winter heating demands. This study aims to evaluate the potential application of DSFs in the harsh cold cities of Northwest China and investigate their role in enhancing energy efficiency in large public buildings. Through energy consumption simulation and a comprehensive evaluation using the TOPSIS entropy weight method, the effects of applying 20 DSF schemes in four cold cities in Xinjiang (Kashgar, Urumqi, Altay, and Turpan) were analyzed. The experimental results indicate that the average EUI energy–saving rates in Kashgar, Urumqi, Altay, and Turpan are 64.75%, 63.19%, 56.70%, and 49.41%, respectively. South–facing orientation is deemed optimal for DSF in Xinjiang cities, with the highest energy–saving rate reaching 15.19%. In Kashgar, the energy–saving benefits of west–facing DSF surpass those of north–facing DSF. Conversely, the order of orientation benefits for other cities is south, north, west, and east. An analysis of heating, cooling, and lighting energy consumption reveals that Box Windows exhibit superior heating energy efficiency, while Corridors are more effective for cooling. This characteristic is also evident in the optimal installation orientation of various types of curtain walls. Given the relatively higher demand for heating compared to cooling in urban areas, Box Windows yields significant benefits when facing south, west, or north; conversely, if there is a high demand for urban cooling, Corridors should be considered in these three directions. Multistorey DSF systems are suitable for east–facing buildings in Xinjiang cities. Selecting suitable DSF schemes based on specific conditions and requirements can reduce building energy consumption. The research findings offer theoretical guidance for designing and implementing DSF in diverse cities in cold regions.

1. Introduction

1.1. Research Background

In the context of global climate change and resource constraints, sustainable development has emerged as a shared objective, particularly in energy consumption, energy conservation, and emission reduction in buildings, which are crucial for advancing global sustainable development goals. With the escalating global focus on environmental preservation and energy efficiency, the construction industry is under pressure to transform [1]. In conventional building energy supply models, heating, ventilation, and air conditioning systems contribute 70% of the total energy consumption [2]. Recent studies have indicated that mechanical ventilation alone could represent up to 25% of a building’s electricity usage [3]. Consequently, reducing energy consumption has risen to the forefront as a pressing global concern. The shift from conventional energy sources to renewable alternatives has become irreversible [4]. China, encompassing roughly 70% of land area in severely cold climate zones, faces a substantial demand for winter heating energy consumption. Considering that heating energy consumption constitutes approximately 34% of the global final energy usage in buildings [5], research on tailored building energy–saving strategies suited to China’s severe cold climates holds practical significance in addressing energy consumption challenges.

1.2. Windows and Building Energy Efficiency

The effectiveness of a building’s facade directly influences its capacity to reduce energy expenses. Inadequate insulation and excessive solar heat gain can result in overheating within the building [6]. As integral components of building envelopes, windows not only offer natural lighting and passive solar gain but also function as primary conduits for heat exchange, with windows accounting for approximately 50% of energy loss [7,8]. Embracing passive building design principles, the industry has introduced a range of innovative solutions and technologies to tackle energy consumption challenges, encompassing diverse glass materials. These solutions include solar shading screens [9], double– or triple–glazed structures [10], advanced smart switchable glass technology capable of modifying glass types and compositions [11,12], semi–transparent photovoltaic (STPV) systems [13,14], and double–skin facade (DSF) systems [15,16].

DSF has gained widespread adoption as an innovative solution for building envelopes [17,18,19]. Compared to single–layer glass facades, DSF offers superior winter insulation and effectively mitigates indoor heat buildup through cavity ventilation during summer [20]. Research indicates that the energy consumption expenses associated with natural ventilation systems are 40% lower than those of conventional air conditioning systems [2]. However, inadequately designed DSF systems can result in improper airflow and excessive solar heat gain during summer, leading to indoor overheating. Moreover, in winter, they may fall short of achieving the intended insulation efficacy [20,21,22]. Consequently, a well–executed DSF design is paramount for enhancing building energy efficiency and advancing sustainable development objectives.

The high performance and efficiency of DSF are contingent upon several factors [23,24]. These include the configuration of double–layer exterior walls, such as Box Windows, Shaft Boxes, Corridors, or Multistorey exterior walls; the airflow source, such as supply air, exhaust air, buffer zones, or outdoor and indoor air curtains; building site parameters, such as orientation, solar radiation, and wind conditions; the utilization of natural or mechanical ventilation; the air cavity width; the incorporation of shading devices; and material composition. This study aims to assess the feasibility of employing DSF in cities characterized by severe cold and cold climates in Northwest China and to evaluate its capacity to enhance energy efficiency in large public buildings within these climates. Through a comprehensive analysis of DSF performance across varying climatic conditions, the research findings will offer theoretical backing for the design and implementation of DSF in such environments. Concurrently, it will elevate individuals’ quality of life and foster the dissemination of green building principles. As the ethos of green building gains traction, the construction industry will increasingly prioritize environmental sustainability and resource preservation, thereby indirectly bolstering sustainable development efforts.

2. Literature Review on Double–Glazed Curtain Walls

The initial DSF system developed in Germany was to withstand severe cold climates and high wind pressures. Subsequently, the integration of DSF has emerged as a prevalent architectural trend. DSF enhances natural lighting, acoustic performance, and thermal comfort, shields structures from harsh weather elements, and efficiently diminishes energy usage while preserving spatial openness and enriching indoor–outdoor visual and informational connectivity.

2.1. Principles of Double–Glazed Curtain Walls

DSF comprises internal and external glass facades along with thermal channels. The fundamental concept involves segregating the thermal channels into distinct temperature strata by regulating the opening and closing of ventilation openings, thereby accommodating various climatic conditions. Illustrated in Figure 1a, where the arrows represent hot air, during the summer months, DSF absorbs solar radiation, warming internal air. Natural convection is induced by leveraging thermal buoyancy and pressure differentials, facilitating heat dissipation through the chimney effect. This process lowers the temperature of the inner curtain wall and diminishes the cooling demand. Conversely, in winter, depicted in Figure 1b, the ventilation openings are sealed to establish a greenhouse effect for optimal insulation. This research comprehensively evaluated thermal efficiency and energy consumption during winter and summer seasons, furnishing a scientific foundation for DSF design and energy conservation strategies.

2.2. Classification of Double–Glazed Curtain Walls

A double–layer glass curtain wall is a complex concept, typically categorized based on three parameters: ventilation type, ventilation unit geometry, and airflow path of cavity type. It can also be delineated by secondary factors such as cavity thickness, exterior wall height, or the automatic nature of ventilation openings. Regarding ventilation type, it predominantly encompasses three categories: natural, mechanical, or mixed ventilation [24,25,26,27].

According to the ventilation path, DSF is typically categorized into two ventilation modes: internal circulation and external circulation [28,29]. In terms of energy consumption, the mechanical ventilation energy consumption of internal circulation DSF is significantly higher than that of external circulation DSF, and its natural ventilation effect is also inferior to that of external circulation DSF. Therefore, in large–scale DSF applications, external circulation DSF has emerged as the prevailing choice due to its energy–saving advantages. Furthermore, external circulation DSF offers various partitioning modes to cater to personalized needs. Given these considerations, this study will simulate and analyze the energy consumption of different types of external circulation DSF to provide references for DSF design.

Regarding geometry, DSF is divided into four types: Multistorey, Box Window, Corridor, and Shaft–Box [30]. Each of these DSF types presents distinct advantages and disadvantages.

Table 1. Characteristics of the four types of curtain wall and diagram table.

Typology	Specificities	Vantage	Drawbacks	Applicability	Elevation plan	Section Plan
Multistorey	There is no horizontal and vertical division of units; generally the whole curtain wall as a ventilation unit; the height of the air cavity is the overall height of the curtain wall.	Simple structure. Good integrity and relatively low cost.	Confusing airflow organization, inability to use heat pressure for ventilation, and overheating of the top end in the summer.	It is not suitable for high–rise large buildings, and the height should be less than 20 m.
Shaft–Box	The air is exchanged in shaft ventilation units by providing a vertical penetration layer on the surface of the building with an air inlet at the bottom and a vent at the top.	Vertical ventilation is good.	The temperature of hot pressing is too high, the ventilation chamber is slender, it makes noise easily and is not conducive to fire prevention.	A height of up to 20 m is desirable, less applicable and very rarely used.
Corridor	The curtain wall is divided horizontally in layers, and the airflow flows horizontally on one or several layers, with air inlet at the bottom, air outlet at the top, and more external corridors.	The gallery style is better for soundproofing between floors and prevents the spread of fire.	It is not conducive to soundproofing and fireproofing between the same floors and tends to waste area.	Suitable for building curtain walls with integral internal partitions.
Box Window	Also known as box or unit type, it divides the curtain wall cavity horizontally and vertically to form unit boxes that can be independently ventilated.	Connections are set up with partitions that are more effective in both sound insulation and preventing the spread of fire.	The ventilation effect of the thermograms effect is limited to the height of the box and is relatively expensive and complex in structure.	Short site construction period without scaffolding, high fire–resistance, especially for high–rise building curtain wall.

outlines their specific characteristics and planar profiles. Box window DSFs are the most prevalent in China, followed by multistorey and corridor DSFs, with shaft–box DSFs less commonly employed.

2.3. Research Status of Double–Skin Facades (DSF)

DSF, a significant energy–saving component in contemporary architecture, has garnered considerable attention in performance assessment and enhancement research. A substantial body of research has explored the influence of shading devices, air gap width, orientation, and other factors on DSF performance, offering a crucial theoretical foundation for enhancing energy efficiency and indoor environmental control in DSF [31,32,33,34]. However, current research needs to be revised. Specifically, the adaptability of DSF in severe cold climate conditions necessitates further investigation. For instance, in hot and humid climatic conditions, Halawa et al. [35] suggested that incorporating adjustable shading devices in the air gap is an effective strategy in hot and humid climates. However, the applicability and efficacy in cold climates require validation. Similarly, Yao [36] examined the impact of adjustable exhaust louvers on naturally ventilated DSF (NVDSF) performance. The findings suggest that louvers opening upwards enhance ventilation, with a 45° angle being optimal. However, additional assessment is imperative to ascertain its effectiveness in severe cold climate zones.

The performance of DSF systems is influenced by solar irradiance levels, making their orientation a crucial factor to consider. Existing research suggests that the orientation of DSF not only affects its ability to harness solar radiation gains for natural ventilation, it also influences its energy efficiency and indoor environmental comfort. For instance, Alvaro [37] assessed a social housing prototype using energy and environmental simulation tools. The simulations revealed that residential buildings with a double–skin structure can significantly improve indoor comfort, particularly in the roof area. Furthermore, Alberto [38] explored the impact of DSF orientation on energy consumption through parametric studies, highlighting that the energy consumption disparity between north–facing and south–facing DSF can be as high as 40%. These studies provide theoretical backing for the correlation between DSF orientation and performance. However, it is essential to recognize that these findings predominantly apply to specific climatic regions and building typologies.

Integrating temperature fields and energy consumption represents a pivotal focus in current DSF research. This research avenue contributes to a more profound comprehension of DSF performance in regulating internal temperature and energy usage. Souza [39] investigated the efficacy of naturally ventilated DSF through numerical simulations and on–site measurements, concentrating on wall airflow and heat convection generated by DSF. The study suggests that DSF modulates indoor temperature by mitigating direct solar radiation, particularly during peak surface temperatures at 4:00 p.m. Kong [17] simulated an office space with adjustable lighting double–skin facades using the TRNSYS–CONTAM coupled simulation approach and validated the results through on–site measurements. Furthermore, the research examined energy consumption patterns during winter and summer in typical cities across five climate zones. This study offers valuable insights for comprehending and optimizing DSF performance in diverse climatic conditions.

Researchers generally advocate for adapting the design of DSF to local climatic conditions to achieve optimal energy efficiency and indoor comfort. Studies by Aleksandrowicz [40] and Flores [41] have highlighted the potential issue of cavity overheating in DSFs during cooling periods in hot climates. This discovery underscores the significance of considering climatic conditions when designing DSF, particularly in hot regions, to avoid inadequate regulation of indoor temperatures. Moreover, wind patterns and solar radiation intensity in different locations are crucial to shaping DSF design. A review of previous research papers on DSFs indicates that temperate climates have been the subject of extensive study. Conversely, current research efforts may need to focus more on cold climate regions, especially those heavily reliant on heating [2].

2.4. Insufficient Existing Research

As illustrated in

Table 2. Summary of relevant literature features on DSF.

Reference	Tool	Type of Building	Location	Koppen Geiger Climate Classifications	DSF Orientation	Seasonal Focus	Type of DSF
[25]	CFD	Experimental Test Cell	Mexicali, Northwest Mexico	Bwh, Tropical and Subtropical Desert	Sunshine–orient	Summer	box–window
[42]	CFD	Office	Isfahan, Iran	BSk, Tropical and Subtropical Steppe	South	Summer	Multistorey
[26]	Energy Plus	Experimental Chamber	Jaipur, India	Bsh, Mid–latitude Steppe and Desert	South	Summer	Box Window
[43]	Energy Plus	Theoretical Model Room	Japan	Cfa, Humid Subtropical	South	Spring and autumn	Box Window
[24]	Experiment	Experimental Test Cell	Montreal, Canada	Dfb, Warm Summer Continental	South	All seasons	Box Window
[44]	Energy Plus	High–rise Office	Tel Aviv, Israel	Csa, Dry–Summer Subtropical	All	Winter and summer	Corridor
[17]	TRNSYS	Office	Tianjin, China	Cwa, Humid Subtropical	Sunshine–orient	All Seasons	RDGCW

, previous studies have predominantly concentrated on south–facing DSF, Box Window DSF, and simulations of standard and typical days in winter or summer. These investigations have yielded valuable insights for the design and enhancement of DSF. Nevertheless, there remains substantial scope for research on the utilization and suitability of DSF in severely cold regions of China. Given the substantial heating requirements in these areas, implementing DSF is significant for achieving tangible energy savings. This study aims to investigate the feasibility of DSF in cities in the severely cold regions of northwest China and evaluate its potential to boost energy efficiency in large public buildings in severely cold climates. Through a comprehensive analysis of DSF performance under varying climatic conditions, this research will offer theoretical backing for integrating DSF design in this particular climate. The research outcomes will aid designers and policymakers in assessing energy–saving alternatives for DSF at an early stage and establish a scientific foundation for enhancing building energy efficiency in severely cold regions. This endeavor will contribute to enhancing the quality of life for individuals and advancing the dissemination of green building principles. With the increasing acceptance of green building practices, the construction industry will emphasize environmental sustainability and resource conservation, thereby indirectly fostering sustainable development.

2.5. Research Methods and Innovations

Summarizing the above analysis, this study focuses on a topic that is less explored in the current academic field, i.e., the application of double–skinned glass curtain walls (DSFs) in severe cold and frigid regions. Given the huge demand for energy in cold and frigid regions and the significant impact of double–glazed curtain wall selection on energy efficiency, this study aims to adopt a systematic approach to selecting curtain wall types with better energy efficiency for cities in cold and frigid regions such as Xinjiang. To accurately evaluate the efficiency of DSF, this study identifies the curtain wall’s orientation and the DSF type as pivotal variables. Considering the climatic characteristics of severely cold regions in northwest China, particularly the cities in Xinjiang, this research selects Kashgar, Urumqi, Altay, and Turpan as representative study sites. To comprehensively analyze the suitability of DSF in these areas, the study has devised 20 distinct DSF configurations to examine the influence of different design choices on building energy consumption.

The synopsis of the innovations in this paper is as follows:

(1)

Software Utilization Innovation: This study utilizes the Honeybee software platform to establish and simulate the energy model of DSF through Energy Plus, providing insights for researchers using Ladybug Tools for comprehensive performance simulations.

(2)

Research Subject Innovation: Focusing on China’s severely cold climate regions, this study evaluates the applicability and energy–saving potential of different orientations and types of DSF throughout the year. Additionally, the selected climate zones, orientations, types, simulation times, and heating, cooling, and lighting metrics bring a new perspective to DSF research.

(3)

Research Method Innovation: The study employs two comprehensive evaluation indicators based on the TOPSIS entropy weight method and the annual EUI energy–saving rate, enhancing the comprehensiveness and reliability of the assessment results and enabling each result to support one another effectively.

3. Research Methodology

3.1. Performance Simulation

This study employed the Ladybug Tools 1.6.0 (LBT) plugin toolkit within the Grasshopper (GH) platform for building performance simulation. GH is a visual programming language software that operates on Rhino. LBT 1.6.0 integrates simulation engines like Energy Plus, Radiance, and Daysim within GH’s framework, facilitating simulation assessments of building daylighting, comfort, and energy usage. The accuracy of the software has been confirmed through experimental validation [45]. Furthermore, the software includes an airflow network model that can simulate both natural and mechanical ventilation in various zones.

The energy simulation plugin Honeybee 1.6.0 module from the plugin toolkit was also utilized in this study, leveraging the Energy Plus engine for building energy simulation within the GH platform. Energy Plus, developed by the National Renewable Energy Laboratory of the U.S. Department of Energy, offers control equations for calculation methods, heat transfer, and mass transfer that are accessible online. In a study by Ahmar et al. [46], two DSF models were compared using CFD and Energy Plus software, revealing no significant differences in the results and showcasing the reliability of Energy Plus in handling complex geometric models. Gennaro et al. [16] assessed the performance of various building energy simulation tools in predicting DSF thermal behavior, with findings indicating that Energy Plus excels in predicting the outdoor air curtain mode, and underscoring its advantages and potential in DSF simulation.

Differences in indoor thermal environments are primarily influenced by outdoor meteorological conditions. A year–long simulation analysis was carried out in this study using the International Weather of Energy Calculation (IWEC) data for building energy assessments. This dataset comprises climate data from selected representative months spanning 30 years in a manner that reflects a typical reference year, deemed to be more closely aligned with real–world conditions.

3.2. Synthesized Assessment

To ensure an objective evaluation of the solutions under each indicator and the selection of the optimal solution, a comprehensive evaluation method is introduced in this study based on the Entropy Weight Method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking the results and analyzing their characteristics. The Entropy Weight Method serves as an objective weighting approach, where the principle dictates that the smaller the variation of an indicator, the lesser information it conveys, resulting in a lower corresponding weight. TOPSIS, a widely utilized comprehensive evaluation method, effectively captures the distinctions among evaluation solutions by fully leveraging the information from the original data. The Entropy Weight TOPSIS evaluation model finds extensive application in the selection of solutions in multi–criteria decision–making [47,48,49]. The specific calculations are outlined as follows:

(1) Establish the sample matrix. Assuming there are $n$ evaluation objects, $m$ evaluation indicators, the sample set and indicator set constitute the original information matrix $(x_{ij}{)}_{n\times m}$, as shown in Equation (1):

$$(x_{ij}{)}_{n\times m}=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \dots & x_{1m}\\ x_{21}& x_{22}& \dots & x_{2m}\\ \dots & \dots & \dots & \dots \\ x_{n1}& x_{n2}& \dots & x_{nm}\end{array}\right]$$

(1)

where, $x_{ij}$ represents the value of the item $j$ indicator of the item $i$ sample; $j=1,2,\dots ,m$.

(2) Standardized Matrix. Let the standardized matrix be $Z$, then perform the operation as shown in Equation (2) on each element of matrix $X$:

$$z_{ij}=x_{ij}/\sqrt{{\displaystyle \sum _{i=1}^n{x}_{ij}^2}}$$

(2)

In this study, the lighting energy–saving rate is negative; therefore, it needs to be further standardized to a non–negative range. This is shown in Equation (3):

$${\tilde{z}}_{ij}=\frac{x_{ij}-\min \left\{x_{1j},x_{2j},\dots ,x_{nj}\right\}}{\max \left\{x_{1j},x_{2j},\dots ,x_{nj}\right\}-\min \left\{x_{1j},x_{2j},\dots ,x_{nj}\right\}}$$

(3)

(3) Calculate the probability matrix $P$. The calculation formula for each element $P_{ij}$ in $P$ is as follows:

$$p_{ij}=\frac{{\tilde{z}}_{ij}}{{\displaystyle \sum _{i=1}^n{\tilde{z}}_{ij}}}$$

(4)

It is easy to verify that: ${\displaystyle \sum _{i=1}^n{p}_{ij}}=1$, which ensures that the sum of probabilities corresponding to each index is 1.

(4) Calculate the information entropy for each index, calculate the information utility value, and normalize to obtain the entropy weight for each index. For the item $i$ index, the formula for calculating its information entropy is as follows:

$$e_j=-\frac{1}{\ln n}{\displaystyle \sum _{i=1}^n{p}_{ij}\ln (p_{ij})}(j=1,2,\dots ,m)$$

(5)

(5) Calculate the redundancy (difference) of information entropy, as shown in Equation (6):

$$d_j=1-e_j,(j=1,2,\dots ,m)$$

(6)

(6) Entropy weight method calculates the objective weights of each index:

$$W_j=d_j/{\displaystyle \sum _{j=1}^m{d}_j}(j=1,2,\dots ,m)$$

(7)

(7) Construct the normalized matrix after weighting:

$${(Z_{ij})}_{n\times m}=\left[\begin{array}{cccc}{w}_1{P}_{11}& w_1{P}_{12}& \dots & w_1{P}_{1m}\\ w_2{P}_{21}& w_2{P}_{22}& \dots & w_2{P}_{2m}\\ \dots & \dots & \dots & \dots \\ w_m{P}_{n1}& w_m{P}_{n2}& \dots & w_m{P}_{nm}\end{array}\right]$$

(8)

(8) Determine the best and worst solutions of the normalized matrix after weighting:

$$Z^{+}=(Z_1^{+},Z_2^{+},\dots ,Z_m^{+})=(\max \left\{z_{11},z_{21},\dots ,z_{n1}\right\},\dots ,\max \left\{z_{1m},z_{2m},\dots ,z_{nm}\right\})$$

(9)

$$Z^{-}=(Z_1^{-},Z_2^{-},\dots ,Z_x^{-})=(\min \left\{z_{11},z_{21},\dots ,z_{n1}\right\},\dots ,\min \left\{z_{1m},z_{2m},\dots ,z_{nm}\right\})$$

(10)

(9) Define the distance between the item $i$ evaluation object and the maximum value $D_i^{+}$, minimum value $D_i^{-}$:

$$D_i^{+}=\sqrt{{\displaystyle \sum _{j=1}^m{(Z_j^{+}-z_{ij})}^2}}$$

(11)

$$D_i^{-}=\sqrt{{\displaystyle \sum _{j=1}^m{(Z_j^{-}-z_{ij})}^2}}$$

(12)

(10) Calculate the comprehensive score of the item $i$ evaluation object, as shown in Formula (13):

$$S_i=\frac{D_i^{-}}{D_i^{+}+D_i^{-}}$$

(13)

3.3. Research Framework

The research framework depicted in Figure 2 is divided into three main sections. The first part entails the establishment of an energy model, which involves the simulation of three qualitative variables: four cities in Xinjiang, four categories of DSF, and the orientations of the DSF facade towards the east, south, west, and north. The building modeling process utilizes Rhino 7.0, where cavity models are constructed based on DSF types, and window opening properties are set. Additionally, building types, construction properties, and indoor thermal disturbances are configured using Honeybee 1.6.0.

The simulation of energy consumption is the focus of the second part. City EPW meteorological data is imported using LBT 1.6.0 to analyze the application of facades. The window opening and closing are set by the HB Window Opening 1.6.0 component, natural ventilation simulation is conducted using HB Airflow Network 1.6.0, and daylighting simulation for analyzing lighting energy consumption is performed using the Radiance core of HB Annual Daylight 1.6.0. Finally, Open Studio is utilized to generate annual data on EUI, cooling, heating, and lighting energy consumption for each scenario.

The third part employs the TOPSIS entropy weighting method to comprehensively evaluate the energy simulation data. By comparing the EUI energy–saving rate and TOPSIS scores of each scenario, the optimal DSF selection and the city’s energy–saving potential are determined. The best orientation of the DSF facade for each city and the corresponding DSF selection are analyzed, taking regional conditions into consideration. Furthermore, the performance of each DSF on sub–energy consumption is analyzed to explore its energy–saving properties, offering a foundation for DSF selection for designers and decision makers in other climate zones.

3.4. Project Overview

The research object selected for this study is a winter fishing pavilion in Xinjiang. The venue showcases modern design and technological elements, offering visitors a delightful indoor fishing entertainment experience all year round. Following Alberto’s recommendation [38], simplifying the basic model is deemed necessary to enhance the speed of the entire simulation process and decrease the likelihood of calculation errors in the software. The simplified building dimensions, as illustrated in Figure 3, include a length of 200 m, width of 100 m, height of 18 m, and a total building area of 20,000 m².

Figure 4 depicts the variations in energy consumption of the Box Window based on different values of the curtain wall opening height and cavity. The vertical axis nomenclature follows the “vent height curtain wall cavity” principle. According to the simulation results in Figure 4a, the results align with the trends observed in existing research [45,50], indicating that 1.2 m performs better compared to 0.9 m, 1.5 m, and 1.8 m. The vent height of 0.3 m is well–validated and effective [51]. Figure 4b presents the energy consumption simulation results for curtain wall vent heights of 0.3 m, 0.6 m, 0.9 m, 1.2 m, and 1.5 m under a 1.2 m cavity. It is worth stating that the chosen parametric approach has been applied several times in the field of energy consumption research and has been effectively practiced in different areas such as city block [52,53,54], single buildings [55,56], and double–layer curtain wall [20,21].

3.5. Selection of Typical Cities

The “GB50176–2016 [57]; Code for thermal design of civil building” categorizes China into five primary climatic zones and 11 climatic subzones, each imposing distinct design requirements. Four typical cities in various climatic subzones of Xinjiang were selected for this study, namely

(1)

Urumqi (Ur): the capital city of Xinjiang, known for its relatively dry climate prone to frequent sandstorms, falling within China’s severe cold Zone C (1C) of thermal zones.

(2)

Altay (Al): Situated in China’s severe cold Zone B (1B) of thermal zones, it is the northernmost city in Xinjiang and experiences the coldest winter temperatures in the region, with a historical low of −52.3 °C.

(3)

Turpan (Tu): Positioned in the Turpan Basin in central Xinjiang, it records extreme summer temperatures reaching 52.2 °C, with surface temperatures often surpassing 70 °C and a high temperature recorded at 82.3 °C. Despite being categorized in the cold Zone B (CB) of thermal zones, it features a climate characterized by “extreme heat and cold”.

(4)

Kashgar (Ka): Classified under China’s cold Zone A (CA) of thermal zones, it showcases typical traits of a dry and hot climate.

Figure 5a depicts the four selected typical cities in Xinjiang, falling within China’s thermal zones as severe cold Zone B (Altay), severe cold Zone C (Urumqi), cold Zone A (Turpan), and cold Zone B (Kashgar). Figure 5b illustrates the yearly monthly temperature distribution of these four cities. Figure 5c reflects the monthly distribution of radiation throughout the year in selected cities. Despite all four cities being situated in China’s severe cold or cold climatic zones, they manifest notable climate distinctions, requiring the selection of suitable DSF systems based on their unique geographical and climatic attributes.

4. Model Construction and Parameterization

4.1. Simulation and Assumptions

To accurately assess the impact of double–glazed curtain wall systems on the energy–saving performance of buildings, the following assumptions have been formulated in this study:

(1)

Thermal insulation boundary conditions [58]: Opaque exterior envelope structures are regarded as thermally insulated surfaces, aimed at eliminating the interference of thermal conduction on the evaluation of curtain wall performance.

(2)

Idealized HVAC system: The system operates efficiently with a COP of 3.6, ensuring that the simulation results focus on the thermal insulation, heat preservation, and energy–saving characteristics of the curtain wall.

(3)

Standardized environmental and operating conditions [59]: All simulations are conducted under fixed indoor temperature settings, consistent lighting and equipment usage patterns, and uniform external climate data, thus ensuring the comparability of results.

(4)

Consistency of internal heat gain: The internal heat gain within the building remains constant throughout all simulations, facilitating the isolation of the independent impact of curtain wall design changes on energy efficiency.

4.2. Geometric Construction of Energy Models

In this paper, the DSF is modeled following the principle of simulating a Trombe wall within the software. This enables the representation of the opening fan associated with the external circulation type DSF being closed during winter and opened in summer. This modeling process involves configuring the cavity section as a small room and defining its properties within the software as a space devoid of any load except for the infiltration load, as illustrated in Figure 6.

4.3. Building Envelope Parameter Setting

The establishment of accurate and reliable base parameters is an essential benchmark and prerequisite when utilizing energy simulation software. A lower K value signifies superior thermal insulation performance of the building envelope structure, leading to reduced energy transfer, decreased energy consumption, and improved indoor environmental comfort. Conversely, a higher K value results in greater energy loss, increased energy consumption, and potential impacts on indoor environment stability. Hence, the selection of the appropriate K value is pivotal for achieving energy–saving objectives in building design and evaluation. To minimize the influence of variables on the results and ensure that changes in the simulation outcomes are solely attributed to the ventilation DSF, the same set of envelope structures is employed in all models. The envelope parameters necessary for the simulation analysis are derived from the building energy efficiency design outlined in the standard GB 55015-2021 [60]—General code for energy efficiency and renewable energy application in buildings.

The “General code for energy efficiency and renewable energy application in buildings” is a national standard issued by the Ministry of Housing and Urban–Rural Development of the People’s Republic of China, which became effective on 1 April 2022. This standard serves as a compulsory engineering construction code, and strict adherence to all its provisions is imperative. Regarding energy conservation, these standards underscore the principle of prioritizing passive energy conservation and advocates for maximizing natural ventilation to enhance the thermal insulation performance of the building envelope structure. Widely acknowledged within the academic community of building energy conservation research in China, this standard serves as a significant academic point of reference for scholars engaged in simulation studies [61], as shown in

Table 3. Setting values for maintenance structure parameters.

Target Setting	Properties	Element Configuration
Exterior walls
Materials:
	1—Extruded Polystyrene Foam Insulation
	2—KPI Clay Hollow Brick
	3—Cement Mortar
Total thickness	36 cm
Heat transfer coefficient	0.45 W/m2·°C	Exterior wall construction diagrams
Roof
Materials:
	1—Extruded Polystyrene Foam Insulation
	2—Reinforced Concrete
Total thickness	20 cm
Heat transfer coefficient	0.3 W/m2·°C	Roof construction diagrams
Window
Materials:	1—Float Glass
Total thickness	15 cm
U values	2.6
SHGC (Solar heating coefficient)	0.4
VT (Visible transmittance)	0.6

.

4.4. Indoor Thermal Disturbance Setting

Human behavior is recognized as one of the key factors influencing a building’s energy consumption and leading to significant discrepancies between operational outcomes and design targets. The energy consumption software is designed to adjust indoor thermal disturbances and human activity schedules to suit the specific context. In this simulation study, an optimal air conditioning system with a cooling and heating COP of 3.6 (as defined by the Chinese standard GB/T 108702014 [62], where COP can represent the refrigeration performance coefficient EER or the heating performance coefficient) was employed as a reference point in the software simulation. The simulation established the indoor temperature range from 18 °C to 26 °C, with additional space heat source parameters delineated in

Table 4. Space heat source parameter setting.

Room Parameter	Set the Value1
Indoor setting temperature (°C)	18 °C~26 °C
Fresh air ventilation rate (m3/(person))	30
Power density of illuminance (W/m2)	10
Personnel density (m2/person)	0.125
Equipment (W/m2)	13

below.

In order to better align with local lifestyle and work demands, a time corresponding to the geographical location was selected for the simulation: the GMT+8 zone time plus 2 h. The detailed calculation process is provided in the appendix. Consequently, to synchronize local time with natural environmental cues like sunrise and sunset, the work hours were designated from 10:00 to 22:00. This adjustment reflects the adoption of Beijing Standard Time in Xinjiang. For instance, considering Urumqi, the longitude variance of approximately 28° to 30° compared to Beijing results in an approximate 2 h time deviation between the two locations. Building regulations in Xinjiang adhere to China’s building codes, with time references based on Beijing Standard Time. While 8:00 Beijing Standard Time corresponds to 6:00 local solar time in Xinjiang, the software settings utilize Xinjiang’s local solar time of 8:00 to represent Beijing Standard Time of 10:00. This study focuses on the winter fishing pavilion, which is characterized by seasonal fluctuations in occupancy rates, lighting, and equipment utilization. Specifically, occupancy rates, lighting, and equipment usage peak in winter and decrease in summer. In conclusion, when configuring the simulation parameters, it is essential to consider both standard configurations and actual circumstances. The simulation timetable is illustrated in Figure 7.

5. Results and Discussion

5.1. Establishment of Evaluation Model Using TOPSIS Entropy Weight Method

5.1.1. Weight Results of Various Indicators Determined by TOPSIS Entropy Weight Method Model

Figure 8 depicts the weights of different sub–energy consumption indicators, calculated using the TOPSIS entropy weight method. Notably, the weight assigned to heating energy consumption is approximately 0.57, with cooling energy consumption receiving a weight of about 0.35. In contrast, the weight allocated to lighting energy consumption is relatively modest, at around 0.09. This distribution of weights aligns with prioritizing heating systems in public buildings in cold regions.

5.1.2. Validation of TOPSIS Entropy Weight Method Model

Sensitivity analysis is conducted to explore the responsiveness of a system or model to variations in system parameters or environmental conditions. The standard procedure entails the maintenance of constant values for other parameters, the modification of a critical parameter within the model, and the monitoring of resultant changes in the model outcomes. In order to evaluate the sensitivity of the weights established using the TOPSIS entropy weight method in this study while keeping other parameters constant, minor adjustments are made to the weights associated with heating, cooling, and lighting energy consumption, followed by an assessment of the scores generated from the TOPSIS entropy weight method.

Subsequently, the standard deviation of the normalized scores obtained after each weight adjustment is calculated to determine the optimal weights. The standard deviation, as illustrated in Equation (14), functions as a statistical measure of the dispersion within a dataset. By comparing the standard deviations across data points, the variability of the data can be determined.

$$\mathrm{s}=\sqrt{\frac{{\displaystyle {\sum }_{i-1}^n{(x_i-\overline{x})}^2}}{n-1}}$$

(14)

where, $\mathrm{s}$ is the sample standard deviation, $x_i$ is the value of each sample, $\overline{x}$ is the sample mean, and $n$ is the number of observations in the sample;

It is important to emphasize that, to maintain data consistency, it is crucial to ensure the uniformity of weight dimensions during the determination of step values. In this study, the step size for the weight of heating energy consumption is approximately 0.00087, for cooling energy consumption is around 0.00055, and the adjustment in lighting energy consumption is calculated by subtracting the values of heating and cooling energy consumption weights from 1. Detailed values for each weight can be referenced in Table S1 in the Supplementary.

Drawing from the sensitivity analysis conducted using the TOPSIS entropy weight method in Figure 9, the minimum standard deviation is 0.00198, attained when the heating weight is 0.565, the cooling weight is 0.349, and the lighting energy consumption weight is 0.085. This outcome confirms the validity of the TOPSIS entropy weight method model and establishes a scientific foundation for subsequent comprehensive assessments.

5.2. Energy Efficiency Assessment of Different Inter–City Glass Curtain Wall Types

In this section, the optimal curtain wall types for various cities in Xinjiang are evaluated and analyzed. Four distinct types of curtain walls are comprehensively utilized to simulate the orientations of the DSF facade for analysis. Energy consumption data for heating, cooling, and lighting are gathered from simulations to evaluate the energy–saving benefits of double–layer curtain walls in different regions and the performance of various curtain wall types in energy consumption. Furthermore, the Energy Use Intensity (EUI) is presented as the energy–saving ratio when compared to single–layer curtain walls and is contrasted with the final scores calculated using the TOPSIS entropy weight method. By comparing the differences between the two comprehensive evaluation metrics, the optimal curtain wall solutions for each city are determined.

5.2.1. Simulation Results Presentation

This study investigates the applicability and energy–saving advantages of double–skin facades (DSFs) in cold regions. The simulation process adopts a year–round approach, considering the annual Energy Use Intensity (EUI) encompassing lighting, heating, and cooling and the individual performances of lighting, heating, and cooling throughout the year. For comparative purposes, the calculation of the EUI indicator is grounded on the energy–saving ratio of single–glazed curtain walls. Specifically

$$\sigma =1-\frac{E^{\prime }}{E_0}\times 100\%$$

(15)

where $E^{\prime }$ represents the energy consumption of EUI per unit area throughout the year obtained from DSF simulation, in kW·h/m²; and $E_0$ represents the corresponding value for single–layer glass curtain wall.

Figure 10 depicts the simulated energy consumption results of typical cities in Xinjiang, utilizing different double–glazed facades and a single–glazed facade for comparison. According to the simulation results of the single–glazed facade as the control, the highest energy consumption among the four selected cities is observed in public buildings in the Urumqi area. Heating energy consumption accounts for over 62% of the total energy consumption, ranking second only to the Altay area, where heating energy consumption represents 85.8% due to Altay’s extremely cold climate characteristics. However, concerning cooling energy consumption, the Kashgar area exceeds the extremely hot Turpan area with a value of 193.16 kWh/m² compared to 121.69 kWh/m². This discrepancy may be attributed to geographical location, as the Kashgar area experiences longer daylight hours and receives more solar radiation, leading to a higher demand for cooling energy consumption. Regarding lighting energy consumption, the Altay and Turpan areas exhibit higher lighting energy consumption compared to the Kashgar and Urumqi areas, primarily due to daylight hours, especially in winter. In Altay and Turpan, the shorter daylight hours in winter result in insufficient natural light, necessitating more artificial lighting. Conversely, the Kashgar and Urumqi areas benefit from longer daylight hours, reducing the reliance on artificial lighting systems.

In terms of the energy–saving potential for heating energy consumption, strong performance is demonstrated by the Box Window facade in the extremely cold regions of Xinjiang, achieving an average heating energy saving rate of 84.7% compared to the single–glazed facade. Among the selected cities, the energy–saving effect of the Box Window facade is more pronounced in Turpan and Kashgar, with energy–saving rates reaching 87.37% and 86.83%, respectively. This can be attributed to Turpan and Kashgar’s wider daily temperature range during winter and the relatively drier climate than Urumqi and Altay. Concerning the energy–saving potential for cooling energy consumption, the Corridor facade outperforms the other options, with an average energy–saving rate of 61.52%. Among the selected Xinjiang cities, the Corridor facade achieves the highest energy–saving rate in Urumqi, reaching 82.42%—the higher wind speeds in the area during the summer influence this. Regarding lighting energy consumption, the Shaft Box and Multistorey facades result in relatively minor losses. Both exhibit a similar impact, with the difference being less than 0.1%.

Figure 11 illustrates the comprehensive evaluation results of energy consumption simulation for implementing double–glazed facades in selected typical cities in Xinjiang. Concerning the energy–saving potential of the cities, based on the EUI energy–saving rates, the ranking of energy–saving potential from highest to lowest is as follows: Kashgar, Urumqi, Altay, and Turpan, with corresponding average EUI energy–saving rates of 64.75%, 63.19%, 56.70%, and 49.41%. This ranking aligns with the scores derived from the TOPSIS entropy weight method. This trend may be attributed to the lower ventilation demand in Altay and Turpan, coupled with the focus on insulation year–round due to prolonged periods of low winter and high summer temperatures. In contrast, Kashgar, compared to Urumqi, experiences drier winters and stronger solar radiation, thereby exhibiting a higher potential for energy savings.

When considering the application of the optimal type of Double–Skin Facade (DSF) in each city, the Corridor and Box Windows in Kashgar, Urumqi, and Altay exhibit higher EUI energy–saving benefits compared to Multistorey and Shaft–Boxes, with differences of up to approximately 10% and a minimum of about 4%. However, the Box Window in Turpan shows the lowest energy–saving benefit, at only 46.64%, nearly 6% lower than the Corridor solution with the highest EUI energy–saving rate. Overall, the application of Corridor DSF in the Xinjiang region yields greater energy–saving benefits than other types, reaching up to 66.57%. Due to the extreme day–night temperature variation in Xinjiang, the horizontally divided ventilation units can effectively leverage the stack ventilation principle to enhance the indoor environment in this region during both winter and summer.

The energy–saving benefits of Multistorey and Shaft–Box Windows show minimal differences of no more than 1% in each city, suggesting that the vertical division of ventilation units has a relatively minor effect on the region. Nevertheless, according to the TOPSIS entropy weight comprehensive score results, Multistorey and Shaft–Boxes receive relatively higher scores. This is attributed to their significant impact on the comprehensive evaluation, particularly influenced by the rate of lighting energy consumption losses, despite their relatively small weight in the calculation.

5.2.2. Discussion

Through simulation analysis, the characteristics of energy consumption and the potential for energy savings in public buildings across various cities in Xinjiang are revealed in this study. The diverse climate in the Xinjiang region, ranging from cold to hot, dictates the varied energy consumption patterns of its public buildings. For instance, the high energy consumption in Urumqi is primarily driven by heating demands. Conversely, Kashgar’s elevated cooling energy consumption can be attributed to its extended daylight hours and intense solar radiation. Moreover, the heightened lighting energy consumption in Altay and Turpan underscores the challenge posed by the short daylight hours during winter in these regions.

In terms of energy–saving measures, box–type facades have demonstrated significant potential for saving heating energy in cold regions, particularly in Turpan and Kashgar. The corridor–type facade exhibits the most effective cooling energy–saving impact in Urumqi, possibly due to the higher summer wind speeds that enhance the efficiency of thermal stack ventilation. The comprehensive evaluation results suggest that Kashgar and Urumqi possess greater energy–saving potential, likely attributed to the arid climate and intense solar radiation during winter in these areas. When choosing facade types, tailored designs should be implemented based on the region’s climatic characteristics and energy–saving requirements to optimize energy–saving outcomes.

5.3. Evaluation of Energy Efficiency of Glass Curtain Walls with Different Orientations of the DSF Facade

In the simulation process of this study, a single facade of the building was chosen for the curtain wall to be installed. At the same time, the other orientations were kept consistent and unchanged. The building was rotated in four cardinal directions to investigate the influence of various curtain wall orientations: east, south, west, and north. This research methodology enables examination of the effects of the curtain wall orientation on performance while maintaining the constancy of other factors.

5.3.1. Simulation Results Presentation

(1)

Kashgar

Figure 12 illustrates the simulated performance of various configurations under different orientations of curtain walls in the Kashgar region. As depicted in Figure 12a, in Kashgar, south–facing curtain walls receive direct and substantial solar radiation, enabling efficient utilization of solar energy for winter heating, consequently decreasing energy consumption. Moreover, the high solar radiation from south–facing walls in summer necessitates effective ventilation and cooling to reduce indoor temperatures and lower air conditioning energy usage. With its effective ventilation and cooling capabilities, the Corridor design excels in insulation during winter and ventilation during summer in this locale. In winter, north–facing curtain walls experience higher heating demands due to reduced solar radiation. The Box Window design, known for its excellent insulation properties, effectively minimizes heating energy consumption during winter. West–facing and east–facing curtain walls receive intense solar radiation in the afternoon and morning, respectively, highlighting the need for shading and ventilation strategies for cooling. With its superior shading and insulation performance, the Box Window design emerges as the optimal choice for both orientations.

According to the analysis of the comprehensive energy–saving rate and TOPSIS entropy weight score depicted in Figure 12b, the effectiveness of curtain wall orientations in the Kashgar region is ranked as follows: south, west, north, and east. In buildings facing south, the Corridor and Box Windows exhibit comparable comprehensive energy–saving rates of 12.7% and 13.01%, respectively. This similarity arises from the consistent solar radiation from south–facing walls throughout the year. The Corridor and Box Windows effectively manage solar radiation, leading to similar energy–saving efficiencies. Further examination indicates that in terms of comprehensive energy–saving potential, west–facing buildings hold a significant advantage over north–facing structures in the implementation of DSF. This discrepancy may be attributed to west–facing buildings receiving more afternoon sunlight, raising indoor temperatures, and necessitating more efficient cooling measures.

Conversely, north–facing buildings are less impacted by direct sunlight, reducing cooling demands. Consequently, the energy–saving effectiveness of north–facing curtain walls primarily relies on insulation performance. East–facing walls receive morning sunlight; however, compared to other orientations, the intensity and duration of radiation are relatively brief. Therefore, the selection of curtain wall type has a relatively minor influence on the overall energy–saving impact. Regarding comprehensive energy conservation, the Box Window emerges as the optimal choice for east–facing curtain walls in the Kashgar region; nevertheless, if the characteristics of public buildings necessitate consideration of specific lighting requirements, priority should be given to integrated curtain walls.

(2)

Urumqi

The simulation results of utilizing ventilated double–layer glass curtain walls in the Urumqi region are illustrated in Figure 13. As depicted in Figure 13a, it is evident that the Box Window exhibits superior comprehensive energy–saving effects in all four orientations within this region. This superiority stems from the prolonged winter duration and lower temperatures in Urumqi, necessitating enhanced insulation performance. Additionally, compared to the Kashgar region, the comprehensive energy–saving performance of public buildings employing Shaft–Box surpasses Multistorey in south–facing and east–facing orientations in Urumqi. This difference can be attributed to the geographical location, as Urumqi receives more solar radiation from the south and east, augmenting the shading and insulation capabilities of Shaft–Box and consequently enhancing their efficiency.

Based on the outcomes of the comprehensive energy–saving rate and TOPSIS entropy weight score depicted in Figure 13b, it is evident that in Urumqi the effectiveness ranking of various curtain wall orientations is as follows: south, north, west, and east. When comparing the results where the effectiveness of west–facing orientation surpasses that of north–facing in the Kashgar region, it is tentatively inferred that this discrepancy is linked to the wind environmental characteristics in Urumqi. North–facing curtain walls in Urumqi can efficiently leverage air walls for heat exchange due to lower winter wind speeds and higher summer wind speeds. In contrast, west–facing curtain walls are more conducive to reducing heat loads for the Kashgar region owing to the intense solar radiation and convective ventilation during the summer. Based on the ranking derived from the comprehensive energy consumption EUI energy–saving rate and TOPSIS entropy weight score, the optimal curtain wall type for the south, north, and west orientations is the Box Window. Regarding east–facing curtain walls, the selection of curtain wall type aligns more closely with that of the Kashgar region. Consider the Box Window for high comprehensive energy–saving requirements; prioritize Multistorey if specific lighting requirements must be addressed.

(3)

Altay

The results of implementing ventilated double–glazed curtain walls in public buildings in the Altay region are depicted in Figure 14 As shown in Figure 14a, the selection schemes for various curtain wall orientations in the Altay region closely resemble those in the Urumqi region. This similarity may be attributed to both regions’ comparable annual temperature distribution, characterized by lower annual temperatures. There is a stronger focus on winter insulation rather than summer ventilation, which aligns effectively with the insulating properties of the Box Window. Within this region, the Shaft–Box demonstrates superior comprehensive energy efficiency compared to the Multistorey, a distinction linked to the relatively longer winters and the heightened emphasis on the building’s insulation performance.

Based on Figure 14b, the EUI energy–saving rate and TOPSIS entropy weight scores of various curtain wall orientations in the Altay region indicate that, according to these two comprehensive indicators, the Box Window performs exceptionally well in the south, north, and west orientations, establishing it as the optimal curtain wall selection for these orientations. Given the prolonged and frigid winters in the Altay region and the brief summers, Box Windows is recommended for buildings facing south, north, and west due to their superior insulation capabilities during winter, a critical factor for Altay, the coldest city in Xinjiang. For buildings facing east, the decision between Multistorey and Box Window may hinge on the lighting requirements of the public building. If specific lighting needs exist, Multistorey may be preferred; however, Box Windows remains the preferred choice for overall energy efficiency objectives.

(4)

Turpan

The simulation results of various curtain wall orientation schemes in the Turpan region are illustrated in Figure 15. As depicted in Figure 15a, Corridors are deemed optimal for all four orientations in this region, primarily attributed to Turpan’s characteristic arid desert climate, renowned for its exceptionally high summer temperatures and intense solar radiation. In such climatic conditions, buildings have a substantial demand for cooling. Corridors typically integrate ventilation corridors, offering a shading effect that facilitates natural ventilation during summer, thereby reducing indoor temperatures and air conditioning loads by minimizing direct sunlight penetration indoors. It is noteworthy that in the Turpan region, the north–facing Box Windows, to some extent, contribute to increased cooling energy consumption, resulting in lower overall energy efficiency compared to Multistorey. This phenomenon may be attributed to the north–facing walls receiving more dispersed radiation during summer, particularly in the morning and evening, leading to elevated surface temperatures and heightened indoor heat loads.

Based on Figure 15b, the ranking of orientation benefits concerning overall energy efficiency and TOPSIS entropy weight scores is as follows: south, north, west, and east. In a region like Turpan, characterized by prolonged summers and high cooling demands, the optimal curtain wall type for buildings facing south, north, and west is the Corridor, ensuring 27.77% heating energy savings and 11.74% cooling energy savings. While Box Windows exhibit superior performance in terms of TOPSIS entropy weight scores and annual EUI energy–saving rates for north and west orientations, they may result in increased cooling energy consumption during summer due to their relatively poor thermal insulation. Multistorey structures are preferred for buildings facing east as they can enhance natural lighting year–round while considering energy efficiency. Corridors can be selected for the east orientation if the primary objective is to reduce overall energy consumption.

5.3.2. Discussion

Based on the simulation results, in the Kashgar region, the south–facing curtain walls can effectively harness solar energy for winter heating, ventilation, and cooling during summer. Both the Corridor and Box Window demonstrate strong performance in this area. The north–facing curtain walls receive minimal solar radiation, with the building’s energy efficiency primarily dependent on heating consumption, making the Box Window the optimal selection. Shading is crucial in west–facing curtain walls, where the Box Window with additional ventilation units exhibits enhanced performance. The east–facing curtain walls receive lower solar radiation for a shorter duration, resulting in a modest reduction in energy consumption when utilizing curtain walls. Multistorey structures are preferred for east–facing orientations with specific lighting requirements.

In the Urumqi region, superior energy efficiency is observed in the east, south, west, and north orientations with the Box Window. The Shaft–Box outperforms the Multistorey in the south and east orientations for public buildings with specific lighting needs. Overall, the Box Window emerges as the preferred option for public buildings in the Urumqi region when considering curtain walls in the south, north, and west orientations. Conversely, the selection of east–facing curtain walls should be guided by lighting requirements.

In the Altay region, the selection of curtain walls mirrors that of the Urumqi region, with the Box Window demonstrating outstanding performance in the south, north, and west orientations, establishing it as the optimal choice. The Shaft–Box surpasses the Multistorey in overall energy efficiency for public buildings with specific lighting needs. Given the prolonged and frigid winters in the Altay region, the preferred recommendation is to utilize the Box Window in the south, north, and west orientations. Concurrently, buildings facing east can opt for the Multistorey based on lighting requirements.

The Corridor is the optimal choice for all four orientations in the Turpan region, effectively mitigating summer temperatures. The north–facing Box Window could increase cooling energy usage, potentially lowering the overall energy efficiency compared to the Multistorey. The Corridor stands out as the preferred option for buildings in the south, north, and west orientations, whereas east–facing structures may contemplate utilizing the Multistorey.

Harbin, another quintessential frigid city in Northeast China, exhibits climatic conditions similar to those of certain cities in Northwest Xinjiang, yet notable distinctions exist. Xinjiang encompasses a vast region with a diverse climate characterized by cold temperatures, aridity, and extended periods of sunlight; influenced by these climatic factors, cities in Xinjiang prioritize the equilibrium between winter heating and summer cooling requirements. Conversely, Harbin is renowned for its protracted and exceedingly cold winters, placing greater emphasis on insulation and heating efficiency in construction practices. Scholar Zou [63] highlights that Urumqi experiences a substantial rise in cooling energy consumption compared to Harbin. This phenomenon is attributed to Harbin’s higher latitude, abbreviated summer season, and reduced demand for cooling. The Urumqi region may necessitate enhanced shading and ventilation strategies to diminish summer cooling energy usage, while Harbin places heightened emphasis on the thermal insulation performance of buildings.

In the study conducted, Altay and Harbin, situated at higher latitudes, exhibit a pronounced requirement for heating energy consumption. The findings of this research indicate that Box Window facades exhibit substantial promise for conserving heating energy in regions characterized by severe cold climates, representing a relatively efficacious energy–saving measure for both Altay and Harbin.

The research presented here demonstrates that south–facing facades exhibit commendable performance in the severely cold and cold regions of Northwest China, aligning with the conclusions drawn in previous studies. Su [64] and Wen [65] have individually examined the energy–saving advantages of photovoltaic shutters in severely cold regions, with their findings indicating that in locales characterized by severe cold, such as Harbin, south–facing windows receiving higher solar radiation outperform other orientations. This observation corresponds to the outcomes of the current study, further underscoring the superior performance of south–facing facades in severely cold regions.

6. Conclusions and Outlook

6.1. Conclusions

This study is focused on energy conservation, employing energy consumption simulation analysis and the TOPSIS entropy weight comprehensive evaluation method. Using Xinjiang, China, as a representative of public buildings in severely cold regions, the study compares and analyzes the energy–saving advantages of various types of Double–Skin Facades (DSFs) and their annual energy savings under different orientations in natural ventilation conditions. It also determines the optimal size and selection of curtain walls for each city and the optimal orientation and associated benefits of each orientation. Additionally, the study systematically examines the energy–saving performance characteristics of different types of curtain walls. The specific conclusions derived from the analysis are as follows:

• Based on the EUI energy–saving rate, the energy–saving potential of each city—Kashgar, Urumqi, Altay, and Turpan—decreases in that order, aligning with the average EUI energy–saving rates of 64.75%, 63.19%, 56.70%, and 49.41%, respectively. This trend also corresponds with the rankings determined by TOPSIS’s entropy weighting method.
• Differences exist in the orientation benefits of installing curtain walls in each city. In the Kashgar region, the benefits of curtain wall orientation vary, with the highest and lowest benefits observed in the south, west, north, and east directions, respectively. Similarly, Urumqi, Altay, and Turpan exhibit consistent patterns of orientation benefits, ranking south, north, west, and east in terms of benefits. The southern orientation is the most advantageous for installing DSFs in Xinjiang cities, with a maximum energy–saving rate potential of up to 15.19%.
• In order to achieve the highest overall energy efficiency in regions characterized by low annual temperatures where winter insulation plays a crucial role, priority should be given to the selection of fully enclosed Box Windows. Conversely, a full corridor layout is recommended in areas with high annual temperatures and where summer ventilation is paramount. The Multistorey orientation facing east is the preferred option for public buildings with specific natural lighting requirements. In cities necessitating summer ventilation and winter insulation, such as Kashgar, Corridors are recommended for south–facing buildings, while Box Windows are advised for north, west, and east orientations. As for public buildings with natural lighting needs, Multistorey structures facing east remain the optimal choice.

6.2. Shortcomings and Prospects

This study conducted simulations to assess the energy–saving advantages of various types of double–skin facades (DSF) in the Xinjiang region, offering insights into sustainable building development. The methodology employed in the study proved effective in evaluating the energy–saving potential of double–skin facades. Nevertheless, certain potential methodological limitations exist. Firstly, the simulation model utilized in the study was constructed based on assumptions and simplified conditions, which could constrain the model’s accuracy in practical applications. For instance, the thermal conductivity characteristics of opaque facade materials should have been considered. Secondly, the study did not consider the potential influence of HVAC systems on building energy consumption. Subsequent research endeavors could improve the model by incorporating these factors more comprehensively.