Energy Performance Analysis and Study of an Office Building in an Extremely Hot and Cold Region

Abstract

China is committed to reaching peak carbon by 2030 and carbon neutrality by 2060. The goals of reducing energy consumption and building a “beautiful China” are being urgently pursued in China. The building studied in this paper is located in the city of Turpan, where the problem of excessive energy use among buildings is significant due to the region’s hot summers and cold winters. Additionally, the fact that the office building studied in this paper has an east–west orientation is significant: the building’s main façade is oriented to the west, comprising a large area of single-layer glass curtain wall. Based on this, this paper proposes optimization strategies from two perspectives of renovation and new construction. Four design options are proposed at the retrofit level: glazed circular curtain wall; glazed enclosed curtain wall; west-facing double-glazed curtain wall circulation combined with south-facing light from the east; recycling of windows on the inside of the exterior glass curtain wall. These suggestions focus on retrofitting the glass curtain wall on the west elevation of the building. Two design options are proposed at the new-build level: west-facing south-oriented light and west-facing north-oriented light. These suggestions were primarily built around the idea of changing the orientation of the windows on the west elevation. The results show that the optimal solution is to implement the west-facing double-glazed curtain wall circulation combined with south-facing light from the east. This program shows a 64.14% reduction in heating energy consumption, a 77.12% reduction in cooling energy consumption, and a 69.67% reduction in total energy consumption. The above research has improved the deficiencies in the performance-based energy efficiency retrofit of office buildings in the region and provided new ideas and suggestions for policymakers and designers to build energy-efficiency retrofits in the early stages.

1. Introduction

In the present postmodernist period, especially during the past forty years (with China’s reform and opening up), China has become the world’s largest energy consumer. China’s coal consumption has increased from 5.86 × 10⁸ tce to 36.2 × 10⁸ tce; simultaneously, carbon dioxide emissions have also been increasing year by year, from 14.24 × 10⁸ ton to 79.55 × 10⁸ ton. The China Building Energy Consumption Research Report, 2020, points out that China’s public building area is 129 × 10⁸ m², with a building operating energy consumption of 3.83 × 10⁸ tce and a unit area energy consumption of 29.73 kgce/m²; the residential building area is 54.5 billion m², with a building operating energy consumption of 6.17 × 108 tce and a unit area energy consumption of 11.32 kgce/m². The construction industry sector is one of the most prominent energy-consuming sectors in China [1]. The energy consumption generated by office buildings in China has been found to account for 30% of the energy consumption of all commercial buildings [2]. In particular, China proposes to reach a carbon peak by 2030 and achieve carbon neutrality by 2060 (dual-carbon policy). The question of how to effectively save energy and reduce emissions at the level of office buildings is of great significance; reducing the energy consumption of office buildings not only saves resources and reduces carbon dioxide emissions, but also beautifies the environment and provides a comfortable and quiet living space for human beings. Thus, it is urgent that we reduce the impacts of a series of energy consumption problems in office buildings. At the same time, such goals are in line with the concept of a “Beautiful China”, as proposed by China. The so-called “beautiful China” concept gives prominence to the construction of ecological civilization and integrates it into all aspects and processes of economic, political, cultural, and social construction. Specifically, this concept is divided into two areas. The first aspect is to ensure the maintenance of a good ecological environment. The second aspect is to practice green production methods and lifestyles. The methods proposed in this paper to reduce energy consumption are green production methods. At the same time, CO₂ emissions are reduced in this optimized design process. The method applies not only to the Turpan region of China but also to the vast majority of hot and cold regions of China. This can achieve reduced energy consumption in office buildings in most parts of China, so that it can provide ideas and guidance for China to achieve carbon peak carbon neutrality. This paper is divided into the following sections: The second part presents a literature review. The third part of the article contains the research methodology and the framework of the thesis. The fourth part presents a simulation analysis of energy consumption in office buildings. The fifth part presents an analysis of office building simulation data. The sixth part presents a discussion of the results. The seventh part presents the conclusion of the article and suggestions for future research.

2. Literature Review

At present, scholars have analyzed and improved research on energy conservation in office buildings from different perspectives. The following paper will analyze their work at several levels.

2.1. At the Building Skin Level

The building skin plays a very important role in influencing the energy consumption of the building, and a green and energy-saving building skin design can significantly reduce the energy consumption of the building. At this level, to reduce the energy consumption of office buildings, most domestic and foreign scholars have studied shading louvers [3,4,5,6,7,8], double-skin façades [9,10], glazing types [11], fixed and dynamic shading forms [12,13,14,15,16,17,18,19], and shading films [20]. Among them, scholars have studied the two aspects of shading louvers and fixed and dynamic shading forms. For example, the following scholars have conducted research in the area of sunshade louvers. Shaeri et al. studied the louver camber of office buildings by examining the solar heat gain and annual energy consumption levels and then went on to analyze the effect of louver cambers on building energy consumption. The results showed that, in the cold climate Tabriz region of Iran, at an optimal inclination of +50°, building energy consumption decreased by 6.3% with southern louvers compared to the consumption level with no louvered shading devices [3]. Huo et al. investigated the energy-saving performance of external shutters for near-zero energy buildings in different regions of China. They found that the total energy-saving potential of the external blinds was effective in improving the energy efficiency of the building and reducing the cooling energy consumption; this finding was achieved through studying the shading properties, such as the angle of the shading panels, their orientation, the window-to-wall ratio, and the building’s orientation [4]. The following scholars have conducted research at the level of shading forms. Koç et al. went on to investigate the impact on energy consumption in office buildings by studying fixed shading devices. The results show that the fixed shading form reduces cooling energy consumption by 37–49% compared to the unshaded case, using high-performance glass types. Compared to the unshaded case, using low-performance glazing types, a 73–78% reduction in cooling energy consumption was found, alongside a 33–70% reduction in total annual energy consumption [13]. Sun et al. investigated the effect of exterior building shading forms on building performance. Their results showed that reasonable exterior shading measures can greatly improve the indoor thermal environment and comfort levels while reducing the energy consumed by the building’s air conditioning system [15]. Investigating the effect of shading control strategies on building performances, Grynning et al.’s results show that automatic control of shading systems can reduce the energy consumed by small south-facing office cubicles [19].

2.2. At the Window Performance Level

It is also becoming more common at this level to reduce energy consumption in office buildings by studying window performance, which can be analyzed by changing some of the physical and chemical properties of the window. The windows prevent heat from entering in the summer, thus reducing cooling energy consumption and providing insulation. At the same time, windows prevent heat loss in winter, thus reducing heating energy consumption and providing insulation. Such an approach ultimately improves the energy consumption of existing office buildings. Currently, at the window level, most scholars at home and abroad have mainly studied from the following perspectives: glass type [21,22,23,24,25,26,27,28,29], polymer-dispersed liquid crystal (PDLC) film [30], dynamic insulation [31,32,33], visible light transmittance [34], smart windows [35], daylight integration [36], and U-value of windows [37]. Among them, at this level, most of the scholars analyze the reduction in building energy consumption through the perspective of glass types. For example, the impact of electrochromic windows on building energy consumption was investigated by Fathi et al. The results showed that the use of electrochromic windows and optimal glass types in the Tehran region can reduce energy consumption by up to 19.89% [21]. Casini scholars have studied the impact of dynamic glazing on building energy consumption. It has been found that active dynamic glazing can effectively reduce energy consumption while enhancing indoor thermo-optic comfort by regulating the amount of incoming infrared radiation as well as visible light [24]. Liao et al. compared the impact of transparent amorphous silicon photovoltaic glass with conventional glass in terms of building energy efficiency. It has been found that transparent amorphous silicon photovoltaic glass can have great potential in reducing cooling energy consumption [28].

However, based on the above studies, the current research level for energy consumption in office buildings mainly focuses on the optimization of the building envelope in terms of external skin and opacity, as well as shading forms. Little research has been performed on glass curtain walls and changing the orientation of windows, and the research on glass curtain walls has only been performed at the photovoltaic level and in terms of glass properties. For areas like Turpan, the current research on glass curtain walls is not comprehensive; for the region, glass curtain wall research should be not only from the aspect of glass performance but also from the aspect of natural ventilation to reduce energy consumption. The objective of this paper is to reduce building energy consumption by improving the glass curtain wall level of office buildings in the Turpan area. Therefore, this paper can provide some guidance for the study of energy consumption of existing office buildings in cold regions.

In addition to this, this paper uses the entropy-based TOPSIS method when making multi-objective decisions on multiple scenarios [38,39,40,41,42,43,44,45,46,47,48]. The methodology used has the following advantages when performing the analysis: The entropy weight method can determine the weight of the indicators by calculating the information entropy of each indicator, and then provide a basis for multi-objective evaluation. The TOPSIS method uses a normalized matrix to derive the optimal and worst solutions, then calculates the distance between each indicator and the optimal and worst solutions; finally, the proximity of each indicator to both is obtained. For example, the following scholars use this evaluation method when making decisions about indicators. Zhu et al. used the entropy-based TOPSIS method to investigate the potential of biochar application in alkaline soils; the results showed that PBDEs had the greatest potential for application [38]. Moridi et al. went on to improve the bad effects of blackouts by studying the factors that affect them. The article analyzes the influencing factors through the best–worst and TOPSIS methods, and the results show that working in grid-restricted areas is the main cause of power outages in urban areas [39]. Liu et al. went on to explore the influence of wind environment factors in building design by investigating the optimal wind environment for building complexes in different seasons. The entropy-based TOPSIS method shows that 60 m high slab buildings staggered in the northern, northeastern, and northwestern directions of the site attain optimal wind field environments [40].

Based on the above analyses and research, the main research objectives of this paper are as follows: (1) To analyze the energy consumption of office buildings in the Turpan area (cold region) using parametric energy consumption software, and to propose strategies for energy consumption reduction and renovation. (2) Establish an energy consumption assessment model for office buildings in the Turpan area using the entropy-based TOPSIS method. The results of the study can provide some guidance and leadership for the future of Turpan (cold region) in terms of the energy consumption of existing office buildings.

The innovations of this paper are as follows:

(1)

To reduce the level of energy consumption, this paper proposes two optimization strategies, renovation, and new construction, in combination with the characteristics of office buildings in Turpan. This strategy can provide designers with ideas to guide them when designing.

(2)

Prioritization of energy-efficient retrofit programs for office buildings in Turpan (TOPSIS method using information entropy and different weighting factors). This can help guide designers when making trade-off judgments.

3. Research Methodology and Thesis Framework

3.1. Presentation of Energy Consumption Indicators

The so-called building energy consumption refers to the energy consumed by existing office buildings in the process of use, which includes the following: heating, air conditioning, hot water supply, lighting, cooking, household appliances, elevators, and other aspects of energy consumption. Due to the special characteristics of the use of office buildings, the energy consumption of the building per unit area is much larger than that of residential buildings.

The total energy consumption expression for the year is:

$$E_{\mathit{total}}=E_{\mathit{heat}}{+E}_{\mathit{cool}}$$

(1)

where $E_{\mathit{total}}$, $E_{\mathit{heat}}$, and $E_{\mathit{cool}}$ represent the total energy consumption of the building, heating energy consumption, and cooling energy consumption (kWh/m²), respectively.

The heating energy consumption equation is expressed as follows:

$${ E}_{\mathit{heat}}=\frac{Q_{\mathit{heat}}}{{A \times \eta }_1{ \times q}_1{ \times q}_2}$$

(2)

where $Q_{\mathit{heat}}$—accumulated annual heating capacity (kWh); $A$—gross floor area (m²); ${\eta }_1$—comprehensive efficiency of the heating system where the heat source is a coal-fired boiler; $q_1$—standardized calorific value of coal (kWh/kgce); and $q_2$—aggregate coal consumption for power generation (kgce/kWh).

The cooling energy consumption equation is expressed as follows:

$$E_{\mathit{cool}}=\frac{Q_{\mathit{cool}}}{{A \times \mathit{COP}}_{\mathit{cool}}}$$

(3)

where $Q_{\mathit{cool}}$—accumulated annual cooling capacity (kWh); ${\mathit{COP}}_{\mathit{cool}}$—integrated performance coefficient of public building cooling system. The ${\mathit{COP}}_{\mathit{cool}}$ value is set according to the “General Specification for Energy Efficiency and Renewable Energy Utilization in Buildings” (GB55015-2021) [49]. That is, it is the “comprehensive performance coefficient of the cooling system for public buildings, taking 3.50”.

Based on the above study, therefore, the energy consumption per unit area (EUI) is selected to be analyzed in this paper. The interpretation of the indicator is as follows: energy consumption per unit area. The unit is kWh/m², which refers to the amount of energy consumed per unit of office floor space. Lower energy consumption means that the building has better performance. Its expression is:

$$\mathit{EUI}=\frac{E_{\mathit{total}}}{A_1}$$

(4)

where $\mathit{EUI}$—building energy intensity (kWh/m²); $E_{\mathit{total}}$—total building energy consumption (kWh); $A_1$—building area (m²).

Office building EUI generally consists of four types: heating, cooling, lighting, and equipment. Heating and cooling energy consumption account for the majority of the energy consumption of all four. Therefore, in this paper, only three aspects—heating energy consumption, cooling energy consumption, and total energy consumption—are studied. In this paper, energy consumption per unit of heating area (HEUI), energy consumption per unit of air-conditioning area (CEUl), and total energy consumption per unit of area (TEUI) are selected for the study.

3.2. Introduction to the Entropy-Based TOPSIS Method Approach

The entropy weight method is an objective method of assigning values, and the entropy value is the weight value of a specific indicator, representing the degree of dispersion of the research object. If the indicator works out to have a large entropy value, it is an indication to some extent that the object has a large change in value, as well as a large weight given to it. The TOPSIS method is an evaluation method for multi-objective decision analysis. This is based on ranking the proximity of a limited number of evaluation metrics to an idealized metric. The TOPSIS method requires a distinction between forward and backward indicators and requires the calculation of forward and backward ideal solutions for each indicator. Each metric is then ranked according to the distance from the positive and negative ideal solutions, and if the distance from the positive ideal solution is small while the distance from the negative ideal solution is large, good performance is indicated. Finally, the above two methods are combined to calculate the weights of the objects using the entropy weight method and then sorted using the TOPSIS method. The specific practices are presented next.

(1)

Establishment of the sample matrix:

A matrix is built with the initial data, m indicators, and n objects, where X = ($X_{\mathit{ij}}$)mn, i = (1, 2, 3…m), j = (1, 2, 3…n), and $X_{\mathit{ij}}$ denotes the value of the j indicator under the i evaluation object.

$$X_{\mathit{ij}}=\left[\begin{array}{ccc}{x}_{11}& \cdots & x_{1n}\\ ⋮& \ddots & ⋮\\ x_{m1}& \cdots & x_{\mathit{mn}}\end{array}\right]$$

(5)

(2)

Calculation of entropy value of evaluation indicators:

$$e_j=\frac{-1}{\mathit{ln\; m}{{\displaystyle \sum }}_1^m{p}_{\mathit{ij}}{\mathit{ln\; p}}_{\mathit{ij}}}$$

(6)

where $e_j$ is the entropy value of indicator j, and $p_{\mathit{ij}}$ is the weight of the j indicator of the i evaluation object in the total.

(3)

Establishment of coefficients of variation for evaluation indicators:

Let $g_j$ be the coefficient of variation for indicator j. The larger the coefficient of variation, the smaller the entropy value.

$$g_j=1-e_j$$

(7)

(4)

Determination of entropy weights for evaluation indicators:

Let $H_j$ be the entropy weight of indicator j. Indicator $g_j$ is normalized to obtain the indicator weights $H_j$.

$$H_j=\frac{g_j}{n}-{{\displaystyle \sum }}_1^n{d}_j$$

(8)

(5)

Distance of the calculated result to the positive and negative ideal solutions:

$${\mathit{Di}}^{+}=\sqrt{{{\displaystyle \sum }}_{j=1}^n{\left(z_{\mathit{ij}}-z_j{}^{+}\right)}^2}$$

(9)

$${\mathit{Di}}^{-}=\sqrt{{{\displaystyle \sum }}_{j=1}^n{\left(z_{\mathit{ij}}-z_j{}^{-}\right)}^2}$$

(10)

where ${\mathit{Di}}^{+}$ and ${\mathit{Di}}^{-}$ are the distances of the i evaluation object from the optimal and inferior solutions, respectively. $z_{\mathit{ij}}$ is the value normalized to $z_{\mathit{ij}}$. $z_j{}^{+}$ and $z_j{}^{-}$ are the optimal and inferior solutions, respectively, consisting of the maximum and minimum values of each column element of the matrix.

(6)

Calculate the relative posting progress for each evaluation indicator:

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

(11)

where C_i is the final score for each evaluated program. This takes the value 0 ≤ C_i ≤ 1.

3.3. Research Framework

The research framework of this paper is shown in Figure 1.

Brief description: The framework of this paper is divided into the following parts. Firstly, the office building model is created using Rhino software (Rhino 7.0), and then the parametric model is built in the GH plug-in. During this period, some boundary condition inputs are required: EPW climate data for the Turpan area, office building parameter information, operational setup conditions needed for the simulation, and so on. Energy models were then created based on both retrofit and new construction perspectives, which in turn resulted in energy consumption data for each scenario. Finally, the TOPSIS method—based on entropy and different weighting coefficients—was used to analyze the energy consumption indexes and to derive the results of the optimal solution.

4. Energy Consumption Simulation Analysis

4.1. Simulation Tools

Some of the more popular building energy performance simulation software are EnergyPlus (EnergyPlus 23.1.0), DeST (DeST 3.0), and DOE-2 (DOE-2.1E). Energyplus, as open-source software, has many advantages of its own. For example, it has high calculation accuracy, a wide popularization range, and convenient parameter input. In this paper, simulation analysis is carried out through Ladybug tools (based on Grasshopper (Grasshopper 1.0)—a free open-source software). The software efficiently integrates in-house proven simulation tools such as EnergyPlus (EnergyPlus 23.1.0), Radiance (Radiance 5.3), Daysim (Daysim 4.0), and OpenStudio (OpenStudio 3.6.0). The tools perform environmental analysis, energy consumption simulation, daylighting simulation, lighting simulation, wind environment simulation, and comfort simulation. Therefore, in this paper, the Honeybee plug-in of the EnergyPlus calculation engine was used for the building energy simulation on the Grasshopper platform; an office building in Turpan is simulated to determine the energy consumption of the different proposals through an energy consumption analysis.

The specific operations are as follows. The office building model was first created using Rhino software, and then the parametric model was built in the GH plug-in. Second, the parametric simulation was transformed into an energy model that allows for energy calculations using the Honeybee module. The Honeybee module is also used to construct the building’s operational settings and boundary condition settings to fine-tune the constraints on the energy model. Finally, the EnergyPlus energy consumption engine is invoked to select the climate data of the Turpan area for building energy consumption calculation—see Figure 2.

4.2. Establishment of Climate Data and Simulation Sites

The location selected for this paper is the city of Turpan. It is located in the central part of Xinjiang Uygur Autonomous Region. This is a key hub location for the New Silk Road and the Asia–Europe Continental Bridge. It is located in the east of the Tien Shan region in an east–west transverse olive-shaped mountain basin, surrounded by mountains on all sides. Its climate is characterized by a typical continental warm temperate desert climate. It also belongs to the cold region in China’s thermal design zoning. It is rich in solar radiation but very dry, rainfall is scarce and often accompanied by high winds, and it covers a total area of 69,713 square kilometers. The annual evaporation in the area is about 3000 mm and the precipitation is only 15 mm. The region experiences hot summers, with temperatures exceeding 36 °C on more than 100 days. Extreme temperatures of up to 49.6 °C in the hottest month. Winters are cold, with extreme temperatures reaching −28 °C in the coldest month—see

Table 1. Climatic characteristics of Turpan.

Representing the City	Annual Precipitation (mm)	Annual Evaporation (mm)	Average Temperature of the Coldest Month (°C)	Average Temperature of the Hottest Month (°C)	Hottest Monthly Temperature Extremes (°C)	Coldest Month Temperature Extremes (°C)	Annual Hours of Sunshine (h)	Annual Solar Radiation (Wh/m2)	Air-Conditioning Degree Days (°C·d)	Heating Degree Days (°C·d)
Turpan	16.4	2839	−7.2	32.7	49.6	−28	3056	2229.3	579	2758

. At the same time, solar radiation is unusually strong in the region, with an average daily total solar radiation rate of 16 KJ. In terms of climate data selection, the more widely used databases are China Standard Year (CSWD), China Typical Meteorological Year (CTMY), and the U.S. Department of Energy (IWEC). Among them, the CSWD meteorological data source is compiled by the China Meteorological Administration (CMA) in cooperation with Tsinghua University and contains typical year-by-year hourly meteorological data from 270 stations in China. Compared to other meteorological data, it is more in line with the meteorological data used in the design of buildings in China. At the same time, it is recognized by Chinese designers for its accuracy and reliability. In this paper, CSWD climate data of the Turpan area in Xinjiang Uygur Autonomous Region were selected. Visualization of climate data through the Ladybug tools—see Figure 3 and Figure 4.

In terms of simulation site selection, the office building is located in downtown Turpan, with a building area of 4956 square meters, one underground floor, and five floors above ground. With a total area of 4024 square meters above ground and 932 square meters below ground, the height of the above-ground building is 32.5 m, and the length is 60 m and the width is 17 m. Building orientation aspects: The building is oriented east–west and the main façade of the building is located on the west side. It is symmetrical from the west side, and the whole façade is in the form of a hidden frame glass curtain wall. It has no metal snap caps on the outer layer of glass. The façade consists of 11 large vertical bars of glass curtain walls. No windows can be opened on the façade. It has a 4 m wide by 12 m long awning at the foyer. On the east side of the building are unitized grid windows, numbering 70, evenly distributed on the façade. The glazing on the north and south sides of the building is a vertical slatted curtain wall that provides light to the corridors. Figure 5 shows the original model of the building and the real photo. The office building plan is shown in Figure 6.

4.3. Construction of the Model

(1)

Setting of boundary conditions.

Thermal parameter setting: Adoption of code limits for thermal parameters of the envelope. The thermal performance of the envelope of public buildings in the cold region of Turpan is set up in accordance with the requirements of the “General Specification for Energy Efficiency and Renewable Energy Utilization in Buildings” (GB55015-2021). As indicated by the field survey, the building has a coefficient of bulkiness of 0.21. At the same time, the following settings were made in terms of setting the thermal design parameters for a typical model of an office building. The heat transfer coefficient of the building facade is 0.50 W/(m²·K). The heat transfer coefficient of the building roof is 0.40 W/(m²·K). The heat transfer coefficient of the building’s exterior windows is 3.0 W/(m²·K). The solar heat gain coefficient for exterior windows is 0.30. The specific heat transfer equation is:

R₀ = R_i + ∑R + R_e

(12)

K = 1/R₀

(13)

where R_i—inside surface heat transfer resistance (m²·K/W); R_e—exterior surface heat transfer resistance (m²·K/W); R₀—enclosure heat transfer resistance (m²·K/W); K—heat transfer coefficient (m²·K/W).

According to the specification, the inner surface heat transfer resistance R_i takes the value of 0.11 m²·K/W in winter and summer, and the outer surface heat transfer resistance R_e takes the value of 0.04 m²·K/W.

Building operation parameter settings: Among the parameters of building operation, there are three that significantly affect the energy consumption of a building: heating, cooling, and personnel activities—see

Table 2. Building model parameter information.

Model Parameters	Input information
Weather data	Location, longitude, latitude, temperature, humidity, solar radiation
Building type	Office buildings
Architectural Geometry	Building form, building orientation, gross floor area, main building functions
Envelope	Window–wall area ratio, glazing (SHGC, U-value, VT), wall construction and roof construction information
Internal loads	Occupancy rate, power density of electronic equipment, building fresh air operation schedule, fresh air volume per capita, area per capita

. In this paper, the office building model runs are set up concerning the General Specification for General code for energy efficiency and renewable energy application in buildings (GB55015-2021) (hereinafter collectively referred to as the Specification). Among other things, in terms of personnel activity parameters, the type of personnel in an office building, the occupancy rate, and the number of personnel all have an impact on the building’s energy consumption. The staff density of the office building in this paper is set at 10 m²/person based on the code requirements for the building. The personnel in the room rate is designed based on the norms, taking into account the actual local work and rest time in Turpan. For the heating and cooling temperature settings in this paper, the temperatures in the Code are referred to and then combined with the local climatic characteristics of the Turpan area. Setting the indoor heating temperature to 18 °C and the cooling temperature to 28 °C. Heating and cooling schedules are set according to local working hours. In terms of setting the parameters of electrical equipment, according to the Code, it is set at 15 W/m². The hour-by-hour utilization rate of electrical equipment is based on the setting of office buildings in the specification as a reference, and combined with the operating hours of local office buildings in the Turpan area as a standard—see Figure 7 and Figure 8. The office building fresh air operation schedule is shown in Figure 9.

(2)

Setting of other conditions.

Performance information statistics: Calibri Aggregator plug-in was selected for the performance metrics obtained from the results processing. In addition, two components were connected to it: Calibri Parameters—used for targets that require statistical performance; Calibri Iterator—used to connect samples that need to be simulated. The folder interface is required to record the location of the computer where the performance target is stored. The obtained performance targets are collected and written in xlsx file format for the next calls.

5. Analysis of Simulation Data Results

In this paper, the energy performance of this building is analyzed from two main perspectives: the study of energy reduction with the purpose of renovation; the study of energy reduction with the purpose of new construction.

5.1. Study of Energy-Saving Retrofit Strategies for Retrofitting Purposes

5.1.1. Initial Building Control Group

To facilitate the analysis of changes in energy consumption of various scenarios against each other. The energy consumption data of the initial building was set as a blank control group. The office building has an initial total energy consumption (TEUI) of 198.89 kWh/m², a heating energy consumption (HEUI) of 114.16 kWh/m², and a cooling energy consumption (CEUI) of 84.73 kWh/m². The retrofit program was analyzed around how to change the glass curtain wall on the west façade to reduce the building’s energy consumption, and four retrofit strategies were provided.

5.1.2. Option 1

The first option is in the form of a double-glazed curtain wall. That is, adding another layer of glass curtain wall to the surface of the existing curtain wall. The cavity between the double-glazed curtain walls is 10 cm. This results in an overall double-skin structure for the west elevation glass curtain wall—see Figure 10.

Figure 11 shows a schematic diagram of the circular section of the double-glazed curtain wall of this solution. The use of double-sided glass curtain walls can effectively provide thermal insulation. This is based on the principle that the curtain wall uses thermal pressure difference and the chimney effect to discharge the air in the cavity in an efficient and timely manner, taking away the corresponding heat. This effectively reduces the surface temperature of the envelope, which in turn reduces the excessive consumption of cooling energy in summer. In addition, the closing of the lower vents during the winter favors the entry of solar radiation into the building. This can reduce the consumption of heating energy in winter to some extent. In Option 1, the double-glazed curtain wall vents on the west elevation of the office building can be opened in the summer and closed in the winter. Option 1 is a double-glazed curtain wall circulation type.

Through numerical simulation, the obtained results show the following findings: Option 1 (recirculating: it can be ventilated in summer and closed in winter) has a heating energy consumption (HEUI) of 41.97 kWh/m², a cooling energy consumption (CEUI) of 20.77 kWh/m², and a total energy consumption (TEUI) of 62.74 kWh/m². This can be seen in the figure. Option 1 has a different degree of reduction in each of the indicators relative to the initial option energy consumption. In terms of heating, the initial energy consumption was 114.16 kWh/m². Option 1 is optimized to 41.97 kWh/m², and the heating energy consumption is reduced by 72.19 kWh/m², a reduction of 63.23%. The reason for the change is that in winter the vents above and below the double-glazed curtain wall are closed, and the sun’s rays shining through the outer glass wall directly heat the air in the hot channels. As the vents are closed, the hot air in the hot channel cannot flow, and the heat on the outer surface of the inner glass curtain wall cannot be emitted, reducing the temperature difference between the inside and outside of the inner glass curtain wall. As a result, it reduces the spread of indoor heat to the outdoors through the inner glass curtain wall, reducing heating energy consumption and providing thermal insulation at the same time. In terms of cooling, the initial energy consumption was 84.73 kWh/m². Option 1 is optimized to 20.77 kWh/m², and the cooling energy consumption is 63.96 kWh/m² less, a reduction of 75.48%. The reason for the change is as follows: In summer, the upper and lower vents of the double-glazed curtain wall are opened at the same time, and due to thermal pressure, the hot air in the channel rises and is discharged from the air outlet, while the outdoor air flows in from the air inlet. The flowing air takes away the heat in the channel and on the outer surface of the inner glass curtain wall, reducing the temperature difference between the inner and outer surfaces of the inner glass curtain wall. At the same time, it reduces the temperature of the outer surface of the inner glass curtain wall and also reduces the heat transfer from the outer surface to the inner surface. This reduces cooling energy consumption and provides insulation at the same time. On the renovation of Option 1, the total energy consumption was reduced by 136.15 kWh/m², saving 68.45%. This is shown in Figure 12.

5.1.3. Option 2

Option 2 is based on Option 1, with the double-glazed curtain wall set to be closed. That is, the change in energy consumption is analyzed in the summer when it is not turned on. This can be seen in Figure 13.

Figure 14 shows a schematic diagram of the closed section of the double-glazed curtain wall of this solution from the numerical simulation. The second scenario is simulated and is known in terms of heating energy consumption. The heating energy consumption changed to 41.56 kwh/m², which is 72.60 kwh/m² lower than the initial heating energy consumption, reducing the energy consumption by 63.59%. The reasons for the change in energy consumption are the same as in Scenario 1. In winter, the vents of the double-glazed curtain wall are closed and the air in the cavity is heated by solar radiation. At the same time, the heat on the outer surface of the inner glass cannot be emitted, reducing the temperature difference between the inner and outer sides of the inner glass curtain wall, reducing the spread of indoor heat to the outside, and thus reducing heating energy consumption. The cooling energy consumption changed from the initial 84.73 kwh/m² to 42.61 kwh/m², a decrease of 42.12 kwh/m², with energy saving reduced by 49.71%. The reduction in energy consumption for refrigeration compared to Option 1 is not significant. The reason for this is that the vents of the double-glazed curtain wall are closed in the summer, making it impossible for the hot air in the cavity to dissipate promptly. The cooler outdoor air cannot enter the cavity, preventing air circulation that would reduce the heat on the outer surface of the inner glass curtain wall. Although the double-glazed curtain wall provides insulation to a certain extent, the energy saving in terms of cooling energy consumption is not significant enough. On the renovation of Option 2, the total energy consumption was reduced by 114.72 kwh/m², saving 57.68%. This can be seen in Figure 15.

5.1.4. Option 3

Option 3 is based on Option 1. That is, the westward direction of the building is under the setting of a circular double-glazed curtain wall. This program then adds external sunshade louvers to the east-facing unit windows, changing them from east-facing to south-facing lighting to further explore and analyze the changes in the building’s energy consumption. This can be seen in Figure 16.

Figure 17 shows the east elevation of this scheme with the windows lighting the southward orientation. These results are from the numerical simulation. The third scenario was simulated in terms of heating energy consumption. The heating energy consumption changed to 40.93 kwh/m², which is 73.23 kwh/m² lower than the initial heating energy consumption, reducing energy consumption by 64.14%. In terms of cooling energy consumption, the cooling energy consumption changed from the initial 84.73 kwh/m² to 19.38 kwh/m², a decrease of 65.35 kwh/m², giving an energy saving reduction of 77.12%. On the renovation of Option 3, the total energy consumption was reduced by 138.58 kwh/m², saving 69.67%. This can be seen in Figure 18.

5.1.5. Option 4

The size of the window-to-wall area ratio directly affects how much cooling and heating energy a building uses. Option 4 uses a combination of glass curtain walls and windows to explore the building’s energy consumption. This is performed in the form of a glass curtain wall on the outside and windows on the inside. Option 4 is cyclic, which is the form of this construction.

The channel can be opened in summer, which can effectively remove the gas in the cavity formed by the curtain wall and the window in time, take away the heat in the cavity, and reducing the energy consumption of the cooling systems. In winter, the passages can be closed to reduce the temperature difference between the inside and outside of the window, reducing heat loss from the inside of the building, which in turn reduces heating energy consumption. This program begins by considering the window-to-wall area ratio for the interior windows. This ensures the energy-efficient design of the buildings for energy consumption while first meeting indoor daylighting codes. According to the provisions in the Energy Conservation Design Standards for Public Buildings, the area ratio of each single-façade window and wall of Category A public buildings in severely cold areas (including light-transmitting curtain walls) should not be greater than 0.60. Therefore, this program only analyzes the window–wall area ratios of 0.1, 0.2, 0.3, 0.4, 0.5, and 0.6 in compliance with the code conditions for discussion and analysis. A comparison of the simulated data reveals that, as the WWR increases, the light performance inside the building is improved; however, at the same time, the building energy consumption also increases. However, since the light does not meet the minimum code requirements for office buildings at WWRs of 0.1, 0.2, and 0.3, it is not considered. This can be seen in Figure 19. Under the working condition of a window–wall area ratio of 0.4–0.6, the energy consumption shows a gradual increase. Therefore, a WWR of 0.4 was used for the energy analysis in this scenario.

Analysis: The architectural model is shown in Figure 20. Figure 21 shows a schematic section of the west elevation, with the curtain wall combined with the windows. In terms of heating, the heating energy consumption has been reduced from the initial 114.16 kWh/m² to 47.34 kWh/m², a reduction of 66.82 kWh/m², reducing the energy consumption by 58.53%. In terms of cooling, the cooling energy consumption has changed from the original 84.73 kWh/m² to 40.76 kWh/m², a reduction of 43.97 kWh/m², which reduces energy consumption by 51.89%. On the renovation of Option 4, the total energy consumption is reduced by 110.79 kWh/m², which is an energy saving of 55.70%. The table of changes in energy consumption is shown in Figure 22.

5.1.6. Summary of Four Retrofit Options

As shown in the figure, the above four scenarios are summarized with the initial scenario to produce a graph of the change in building energy consumption carried out under the retrofit perspective.

In Figure 23, one can see the case of the remodeling as a design. Option 3 is the most effective in reducing energy consumption, especially in reducing cooling energy consumption. The optimized cooling energy consumption is 19.38 kwh/m², which is 65.35 kwh/m² less than the original cooling energy consumption, a reduction of 77.12%. This not only meets the average value of 39 kWh/m² for cooling energy consumption of various types of public buildings in cold regions in the code, but is even lower than the standard value. Scenario 3, in terms of heating, has an optimized heating energy consumption of 40.93 kwh/m², which meets the requirements of the normative standard value. The other programs have different degrees of reduction in energy consumption relative to the original energy consumption, although the energy-saving effect is more obvious. However, the rankings are still not very good compared to Option 3. In summary, at the level of office building retrofit design, a total of four scenarios are discussed, and the energy efficiency of the four scenarios is ranked in order: Option 3 > Option 1 > Option 2 > Option 4.

5.2. Research on Energy-Saving Retrofit Strategies for New Construction

5.2.1. New Construction Program Reference Building

This part mainly analyzes the building in the original location orientation: the area is unchanged in the case of the new construction, as the design point of view is to reduce the energy consumption of the existing building. In the case of new construction, since the orientation of the building cannot be changed, the energy consumption generated by the large glass curtain wall on the west façade of the building is large; the energy consumption of the building is studied by changing the orientation of the west façade of the building. Here, a baseline energy model is first developed. The boundary conditions for the baseline model of the new building are as follows: The heat transfer coefficient of the building facade is 0.50 W/(m²·K). The heat transfer coefficient of the building roof is 0.40 W/(m²·K). The heat transfer coefficient of the building’s external windows is 1.7 W/(m²·K). The solar heat gain coefficient for exterior windows is 0.30. The operating parameter boundary conditions for the building are the same as above. The glass curtain wall on the west façade of the building is designed as a window, and then the east–west façade orientation remains unchanged. Based on this, a baseline energy model was developed. The final model of the building has a TEUI of 133.15 kWh/m², an HEUI of 65.04 kWh/m², and a CEUI of 68.11 kWh/m².

5.2.2. New Construction: Option 1

West-facing light, south-oriented option: by changing the orientation of the windows, the west-facing light from the windows on the west elevation is changed to the south-facing light.

Building orientation also has a great potential for energy efficiency in buildings. This is mainly reflected in the heat radiation from the sun to the building and convective heat transfer from natural ventilation to the building. Good orientation allows the building to reduce solar radiation in the summer while increasing natural ventilation and thus reducing cooling energy consumption. And good orientation in winter allows the building to avoid the dominant winter winds while increasing solar radiation and thus reducing heating energy consumption. Since the building is oriented east–west and the west-facing windows are exposed to western sunlight, making the building’s energy consumption higher, the building’s reduction in energy consumption was explored by changing the west-facing orientation. This can be seen in Figure 24. This diagram shows the building’s west-facing light changing to south-facing light by changing the orientation.

The new construction Option 1 starts by changing the orientation of the windows from west light to south light. Carrying out such a design not only cuts down the impact of western sunlight but also reduces energy consumption. In addition, it is known from the code that the window-to-ground area ratio for offices is required to be 1/6 in the daylighting code for residential buildings. According to the “Building Lighting Design Standards”, the window-to-floor area ratio of 1/6 with the lighting class IV lighting coefficient standard requirements have a more consistent correspondence. From the concept of the window-to-ground ratio, it can be seen that the floor area of the building’s west-facing office is 54 m², so the area of the west-facing south-facing window is 9 m². Therefore, in the case of the new constructions, Option 1 has its west-facing windows changed to south-facing windows. In terms of cooling, the cooling energy consumption was reduced from the initial 68.11 kWh/m² to 22.95 kwh/m², a reduction of 45.16 kWh/m², reducing energy consumption by 66.31%. Analysis of causes of change: During the summer months, the change from west-facing light to south-facing light will reduce direct solar radiation into the interior of the building. This reduces the amount of heat entering the room, which in turn reduces cooling energy consumption while also reducing the effects of Western exposure. In terms of heating energy consumption, the heating energy consumption has changed from 65.04 kWh/m² to 47.35 kwh/m², a reduction of 17.69 kWh/m², reducing energy consumption by 27.19%. Analysis of causes of change: As the dominant wind direction in the area is northwest all year round, the building was oriented east–west before the remodeling. Being under the dominant wind direction in winter, air infiltration through window gaps is more severe due to the poor airtightness of the windows. After converting the west-facing windows to be south-facing windows, the main façade windows avoid the dominant local wind direction, reducing cold air infiltration and thus heating energy consumption. For the design of the new construction Option 1, the total energy consumption was reduced by 62.85 kWh/m², an energy saving of 47.20%. The new Scenario 1 model is shown in Figure 25. A graph of the change in energy consumption for the new Scenario 1 is shown in Figure 26.

5.2.3. New Construction: Option 2

West-facing light changed to north-facing light: by changing the orientation of the windows, the west-facing light from the windows on the west elevation is changed to a north-facing light.

This scenario is along the same lines as the new construction Scenario 1; however, instead of changing the west-facing windows to south-facing windows, they are changed to north-facing windows. Next, we go on to explore the changes in the building’s energy consumption. This can be seen in Figure 27. In terms of cooling, the cooling energy consumption was reduced from the initial 68.11 kWh/m² to 22.07 kWh/m², a reduction of 46.04 kWh/m² and a reduction of 67.60% energy consumption. In terms of heating, the heating energy consumption has changed from 65.04 kWh/m² to 49.92 kWh/m², a reduction of 15.12 kWh/m² and a reduction of 23.25% in energy consumption. On the design of the new construction scheme II, the total energy consumption was reduced by 61.16 kWh/ m², an energy saving of 45.93%. This can be seen in Figure 28.

5.2.4. Summary of Two New Construction Options

As shown in the figure, the above two scenarios are summarized with the baseline scenario to produce a graph of the change in building energy consumption performed under the new construction perspective.

As can be seen in Figure 29. Under the scenario with new construction as the design, Option 1 has the most significant reduction in total energy consumption. In terms of cooling, the energy consumption was reduced by 45.16 kWh/m², a reduction of 66.30%, to 22.95 kWh/m² after optimization. In terms of heating, the energy consumption was reduced by 17.69 kWh/m², a reduction of 27.19%, to 47.35 kWh/m² after optimization. This meets the average value of 39 kWh/m² for heating and cooling energy consumption of public buildings in the code. In addition, the total energy consumption is reduced by 62.85 kWh/m², saving 47.20%, which is the most obvious energy-saving effect in the two new construction programs. The energy savings of the two new construction options are ranked in the following order: Option 1 > Option 2.

6. Discussion

6.1. Entropy-Based TOPSIS Method Evaluation Model Creation

In this paper, an entropy-based model creation of the TOPSIS scheme is carried out with the use of Python (version 3.11) to visualize and analyze the data.

Step 1—Identification of the evaluation indicators: based on the above discussion, three metrics were selected—total energy consumption (TEUI), heating energy consumption (HEUI), and cooling energy consumption (CEUI). This was further refined into eight sub-schemes based on two design options: retrofit and new construction. The eight programs are: Option 1 (initial construction), Option 2 (double-glazed circular curtain wall), Option 3 (double-glazed enclosed curtain wall), Option 4 (west-facing double-glazed curtain wall circulation combined with south-facing light from the east), Option 5 (recycling of windows on the inside of the exterior glass curtain wall), Option 6 (new construction of program reference building), Option 7 (new construction scheme: west-facing with south-facing light), Option 8 (new construction scheme: west-facing with north-facing light). A total of eight sets of data were analyzed to determine the nature of each indicator, culminating in the TOPSIS model.

Step 2—Individual indicator types were identified as follows:

(1)

Total energy consumption (TEUI): This is a very small indicator and the lower (smaller) the value of the indicator, the better.

(2)

Heating energy consumption (HEUI): This is a very small indicator and the lower (smaller) the value of the indicator, the better.

(3)

Cooling energy consumption (CEUI): This is a very small indicator and the lower (smaller) the value of the indicator, the better.

The smaller the value of the above three indicators, the lower the energy consumption of the office building, the more energy-efficient it will be, and the higher the energy-saving benefits.

Step 3—The entropy weighting method determines the weight information, information entropy value, and information utility value of each indicator. This is shown in

Table 3. Entropy weighting method weights, information entropy values, and information utility values.

Type	Total Energy Consumption (TUI)	Heating Energy Consumption (TUI)	Cooling Energy Consumption (TUI)
Information entropy values (e)	0.92	0.93	0.90
Information utility value (d)	0.07	0.06	0.09
Weights (%)	31.45	28.50	40.03

.

6.2. Evaluation Model Visualization

Use visualization software to manipulate the data.

Table 4. TOPSIS Evaluation Method Calculations.

Type	Positive Ideal Solution (D+)	Negative Ideal Solution (D−)	Composite Score	Sort
Option 1	0.999997	0.000000	0.000000	8
Option 2	0.018312	0.981921	0.981691	2
Option 3	0.244821	0.813673	0.768707	5
Option 4	0.000000	0.999997	1.000000	1
Option 5	0.240183	0.787174	0.766212	6
Option 6	0.583462	0.474277	0.448387	7
Option 7	0.070855	0.930557	0.929244	3
Option 8	0.080622	0.930394	0.920256	4

shows the results of the matrix calculation of the TOPSIS evaluation method. The ranking results are based on the distance of each solution from the positive and negative ideal solutions, respectively.

The final score and ranking of the entropy-based TOPSIS method are derived by using the visualization chart below for presentation. This can be seen in Figure 30. The graph indicates that the higher a program scores, the higher the square logo rises. As can be seen from the graph, Option 4 scores the highest and therefore has the highest body block rise. Based on this, the final order of advantages and disadvantages of each program is as follows: Option 4 > Option 2 > Option 7 > Option 8 > Option 3 > Option 5 > Option 6 > Option 1.

6.3. Model Creation of TOPSIS Method Based on Different Weighting Coefficients

In this subsection, the ranking scores of the scenarios are analyzed through varying the weighting factors. Two scenarios are derived from this, based on changing the different weighting factor perspectives. The first scheme is to set TEUI, HEUI, and CEUI as the preferred targets sequentially, with the weight of the preferred target being 1 and the others set to 0. The second scenario was to set the weight of the three preferred targets (TEUI, HEUI, and CEUI) to 0.33 for score ranking situation analysis.

6.3.1. The First Scheme of Different Weighting Coefficients

In this subsection, the program scores are analyzed based on the weight settings for the different preferred goals.

Table 5. The case of the TOPSIS method when the TEUI weight is 1.

Type	Positive Ideal Solution (D+)	Negative Ideal Solution (D−)	Composite Score	Sort
Option 1	0.999998	0.000000	0.000000	8
Option 2	0.017534	0.982463	0.982465	2
Option 3	0.172174	0.827823	0.827825	5
Option 4	0.000000	0.999998	1.000000	1
Option 5	0.200533	0.799464	0.799466	6
Option 6	0.525616	0.474382	0.474383	7
Option 7	0.072088	0.927910	0.927911	3
Option 8	0.077788	0.922209	0.922211	4

shows the case when the TEUI weight is 1 and the HEUI and CEUI weights are 0.

Table 6. The case of the TOPSIS method when the HEUI weight is 1.

Type	Positive Ideal Solution (D+)	Negative Ideal Solution (D−)	Composite Score	Sort
Option 1	0.999997	0.000000	0.000000	8
Option 2	0.014201	0.985795	0.985798	3
Option 3	0.008603	0.991394	0.991396	2
Option 4	0.000000	0.999997	1.000000	1
Option 5	0.087532	0.912465	0.912467	4
Option 6	0.329235	0.670761	0.670763	7
Option 7	0.087668	0.912328	0.912331	5
Option 8	0.122763	0.877233	0.877236	6

shows the case when the HEUI weight is 1 and the TEUI and CEUI weights are 0.

Table 7. The case of the TOPSIS method when the CEUI weight is 1.

Type	Positive Ideal Solution (D+)	Negative Ideal Solution (D−)	Composite Score	Sort
Option 1	0.999996	0.000000	0.000000	8
Option 2	0.021270	0.978726	0.978729	2
Option 3	0.355469	0.644527	0.644529	6
Option 4	0.000000	0.999996	1.000000	1
Option 5	0.327160	0.672836	0.672838	5
Option 6	0.745674	0.254322	0.254322	7
Option 7	0.054628	0.945368	0.945371	4
Option 8	0.027390	0.972606	0.972609	3

shows the case when the CEUI weight is 1 and the TEUI and HEUI weights are 0.

Analysis: With a TEUI weight of 1, the ranking of the individual scenarios of the TOPSIS method is as follows: Option 4 > Option 2 > Option 7 > Option 8 > Option 3 > Option 5 > Option 6 > Option 1. With the HEUI weight of 1, the ranking of the individual scenarios of the TOPSIS method is as follows: Option 4 > Option 3 > Option 2 > Option 5 > Option 7 > Option 8 > Option 6 > Option 1. With a CEUI weight of 1, the ranking of the individual scenarios for the TOPSIS method is as follows: Option 4 > Option 2 > Option 8 > Option 7 > Option 5 > Option 3 > Option 6 > Option 8. It can be seen that, in the ranking of the three scenarios discussed, Option 4 scored the highest in all of them and ranked first.

6.3.2. The Second Scheme of Different Weighting Coefficients

In this subsection, the weights of TEUI, HEUI, and CEUI are set to 0.33 for visualizing the result analysis, respectively. This is shown in

Table 8. The case of the TOPSIS method with TEUI, HEUI, and CEUI weights of 0.33 each.

Type	Positive Ideal Solution (D+)	Negative Ideal Solution (D−)	Composite Score	Sort
Option 1	0.999997	0.000000	0.000000	8
Option 2	0.017903	0.982332	0.982100	2
Option 3	0.228091	0.833380	0.785118	5
Option 4	0.000000	0.999997	1.000000	1
Option 5	0.227236	0.800925	0.778987	6
Option 6	0.559970	0.496534	0.469978	7
Option 7	0.072725	0.928633	0.927373	3
Option 8	0.085385	0.924837	0.915478	4

.

Analysis: Based on the results of the above table, the ranking of the various scenarios of the TOPSIS method is as follows: Option 4 > Option 2 > Option 7 > Option 8 > Option 3 > Option 5 > Option 6 > Option 1.

7. Conclusions

7.1. Limitations of the Article Study

The research in this paper does not take into account the internal thermal comfort of office buildings and the wind direction trend in the internal wind environment part of the office buildings. Subsequent studies can build on the content of this paper to solve the problem in many ways. The analysis of the energy consumption indicators also deserves further discussion. In addition, this study used climate data from a typical meteorological year; while these data can represent the climate characteristics of the region to some extent, there remains potential for a partial error within the climate data of the field survey. At the same time, there is a difference between the accuracy of the software and the measured energy consumption data, and field testing and other methods can be considered for subsequent research on energy consumption and related issues.

7.2. Article Conclusions

This paper takes an existing office building in the Turpan area as an example. Numerical simulation of different energy-optimization methods is carried out based on the Honeybee energy plug-in, and three forms of energy analysis are used in terms of energy reduction strategies: double-glazed curtain wall, glass curtain wall–window combination, and changing window orientation. The optimized measures are visualized in the form of three quantitative indicators: total energy consumption (TEUI), heating energy consumption (HEUI), and cooling energy consumption (CEUI). The entropy-based TOPSIS method is also used to rank and discuss the different energy-consumption scenarios. The conclusion is that the adoption of Option 4 (west-facing double-glazed curtain wall circulation combined with south-facing light from the east) can effectively reduce the energy consumption of the existing office building by 69.67%. Based on the above discussion and the numerical simulation study of the building’s performance, the main conclusions are presented here.

Optimization strategy: The initial energy simulation of an office building in the Turpan area revealed that the energy consumption per unit of floor area of the building is too high. On this basis, two optimization strategies are proposed.

In terms of remodeling perspectives, Option 1 is a double-glazed circulation-type curtain wall and Option 2 is a double-glazed enclosed-type curtain wall. Scenario 1’s heating energy consumption (HEUI) is reduced by 63.23%, a reduction of 72.19 kWh/m². The cooling energy consumption (CEUI) is reduced by 75.48%, a reduction of 63.96 kWh/m². The total energy consumption (TEUI) is reduced by 68.45%, a reduction of 136.15 kWh/m². Scenario 2’s heating energy consumption (HEUI) is reduced by 63.59%, a reduction of 72.60 kWh/m². The cooling energy consumption (CEUI) is reduced by 49.71%, a reduction of 42.12 kWh/m². The total energy consumption (TEUI) is reduced by 57.68%, a reduction of 114.72 kWh/m². The third option is to add exterior solar shading louvers to the east-facing side of the building, based on Option 1. Scenario 3’s heating energy consumption (HEUI) is reduced by 64.14%, a reduction of 73.23 kWh/m². The cooling energy consumption (CEUI) is reduced by 77.12%, a reduction of 65.35 kWh/m². The total energy consumption (TEUI) is reduced by 69.67%, a reduction of 138.58 kWh/m². The fourth option is a glass curtain wall combined with a window in a circular form. Scenario 4’s heating energy consumption (HEUI) is reduced by 58.53%, a reduction of 66.82 kWh/m². The cooling energy consumption (CEUI) is reduced by 51.89%, a reduction of 43.97 kWh/m². The total energy consumption (TEUI) is reduced by 55.70%, a reduction of 110.79 kWh/m².

In terms of the new construction angle, the first option is southward facing, with west-facing light; the second option is northward facing, with west-facing light. Scenario 1’s heating energy consumption (HEUI) is reduced by 27.19%, a reduction of 17.69 kWh/m². The cooling energy consumption (CEUI) is reduced by 66.31%, a reduction of 45.16 kWh/m². The total energy consumption (TEUI) is reduced by 47.20%, a reduction of 62.85 kWh/m². Scenario 2’s heating energy consumption (HEUI) is reduced by 23.25%, a reduction of 15.12 kwh/m². The cooling energy consumption (CEUI) is reduced by 67.60%, a reduction of 46.04 kwh/m². The total energy consumption (TEUI) is reduced by 45.93%, a reduction of 61.16 kwh/m².

For the decision-making component, the TOPSIS method (based on entropy and different weighting coefficients) was used to analyze the energy consumption indicators. In the entropy-based TOPSIS method, the weights, the information entropy values, and the information utility values—determined according to its methodology—indicate that the cooling energy consumption (CEUI) has the largest weight. Therefore, when designing aspects related to energy-conservation research, the Turpan region should prioritize the impact of cooling energy consumption on the energy consumption of existing office buildings. Then, in turn, the decision-makers should consider the impact of total energy consumption and heating energy consumption on the energy consumption of existing buildings, based on specific building designs. For example, the impact of total energy consumption needs to be prioritized when conducting an economic analysis of an office building that requires global costs as an evaluation indicator of the building’s life cycle. The impact of heating energy consumption needs to be prioritized when designing heating decisions for office buildings in winter.

The results of the above research can be seen to mainly analyze the reduction in the energy consumption of office buildings in the Turpan area as the research objective. In future research, issues such as thermal comfort conditions inside buildings and the direction of the wind environment can be included in the study. This will allow for improved completeness and comprehensiveness in this research on office building performances. In addition, based on the research in this paper, the topic of future research can be extended outward, which can be combined with the content of this paper to study the carbon emissions and economies of office buildings from the perspective of the whole life cycle of the buildings.