Optimization of the Energy-Saving Building Envelopes in Regional Climate

Abstract

For a large number of monolithic buildings in the Wenzhou area, serious cold air infiltration occurs at night due to the poor insulation performance of the enclosure structure. This results in the indoor temperature often falling below the thermal comfort zone, which is compounded by high relative humidity. Various types of energy-efficient buildings, considering different envelope structures (exterior walls, roofs, exterior windows, and shading designs), were constructed, taking into account both structural characteristics and residents’ energy consumption habits. The building environment and energy consumption during the transition period (i.e., summer and winter) were analyzed using the ideal solution similarity ranking preference method (TOPSIS) and the building energy simulation software, EnergyPlus 9.5. This analysis aimed to identify four energy-efficient building models closest to the ideal solution. Comparing the indoor environment parameters and load values of the energy-saving buildings with those of the basic building yielded the following results: the average building load of the energy-saving buildings was 79.48 to 122.00 W lower than that of the basic building. The average temperature difference between the exterior walls of the energy-saving buildings and the interior temperature was 0.6 to 1.45 °C lower than that of the basic building. Similarly, the average temperature difference between the exterior windows and the interior temperature of the energy-saving buildings was 0.56 to 0.98 °C lower than that of the basic building. Additionally, the average temperature difference between the roof and room temperature of the energy-saving buildings was 0.54 to 0.39 °C lower than that of the basic building. Furthermore, the average indoor temperature of the energy-saving buildings was 1.50 to 2.11 °C higher than that of the basic building. In addition, when compared with the basic building, the energy-saving buildings could save 5.66 to 16.39 kg of standard coal and reduce CO₂ emissions by 1.60 to 4.47 kg during the transition period. The study of the energy-saving envelope structure combination form in Wenzhou provides a reasonable basis for building an energy-saving envelope structure combination form in the tropical monsoon climate area, which is of great significance for reducing building energy consumption.

1. Introduction

Buildings are the basic living areas of human beings and tend to consume a large amount of energy. In fact, in both developed and developing countries, building energy consumption accounts for a large proportion of the total energy consumption. At present, the building industry in China is shifting from the extensive development of expanding space to intensive developments that are focused on the quality of the residential space and living comfort [1]. Here, both the energy-saving aspect and the indoor environment must be considered when selecting the specific building envelope system. In terms of the single buildings in Wenzhou area, a number of crucial issues require urgent attention. Specifically, during the transition between seasons, serious cold air penetration occurs in the residential buildings at night, while the envelope systems tend to exhibit poor insulation performance with little difference between the indoor and outdoor temperature, and the indoor temperature at night is often lower than that of the thermal comfort zone. Meanwhile, the high relative humidity generally results in poor thermal comfort. The conflict between the energy-saving aspect of building envelopes and the comfort of the living environment is thus becoming increasingly prominent with the improvement in the envelope’s performance the common requirement of most residential buildings and a major issue that must be resolved by the construction industry [2,3].

The energy-saving design of the envelope is largely aimed at improving the thermal insulation performance, reducing the influence of external adverse conditions on the indoor environment, and reducing the energy consumption while ensuring a comfortable indoor environment is maintained. A large number of countries have attached great importance to the research on the energy-saving aspects of the envelope with a series of studies carried out. For example, Diao et al. [4], Stazi et al. [5], Cabeza et al. [6], Pan et al. [7], and Fang et al. [8] have conducted comparative analyses of the external insulation effects of different types of wall, identifying the types with good thermal insulation performance that meet the requirements of the attendant standards in terms of energy-saving efficiency and comparing the building energy-saving rates in different seasons. Meanwhile, Daouas et al. [9] analyzed and verified the envelope-related heat transfer theory, determining the limit of the envelope heat transfer coefficient in both winter and summer. Elsewhere, Guo et al. [10] simulated and analyzed a residential building in Shanghai and found that the energy-saving effect of the external wall insulation tended to be considerable with a continuous heating pattern but extremely small with an intermittent energy consumption pattern. Jian et al. [11,12] calculated the thermal load value of external walls with insulation materials of different thicknesses and determined the law that describes how the thickness of the insulation layer changes with the energy-saving effect. Meanwhile, Tu et al. [13,14,15,16] proposed evaluating the overall thermal performance of residential building envelopes in areas characterized by hot summers and cold winters by using the system evaluation index of the thermal performance of building envelopes, including in terms of the heat transfer and solar radiation coefficients, while Tummu et al. [17,18] conducted experiments related to the cooling load of air conditioners under different window/wall ratios and different building envelopes. Here, the results indicated that when there are no windows, both the internal and the external insulation can reduce the building’s energy consumption, while with an increase in window area, the internal insulation can still reduce the energy consumption but the external insulation exhibits an “anti-energy-saving” characteristic. Elsewhere, Udaykumar et al. [19] analyzed the comfort performance of an Ahmedabad unit building and devised a prediction formula for evaluating the comfort of unit buildings according to the existing comfort evaluation method that is in line with international standards. Crawley et al. [20,21] investigated a new generation of building energy simulation software. The results revealed that the EnergyPlus 9.5 software incorporates numerous innovative simulation features, such as variable time steps, a user-configurable modular system, integrated zone simulation based on heat and mass balances, and customized input and output data structures. It can facilitate the development of third-party modules and interfaces to estimate building energy consumption. Garcia et al. [22] proposed a new MATLAB ultrasound simulator (SIMUS) belonging to the MATLAB (R2017b) Ultrasound Toolbox (MUST). The SIMUS ultrasound simulates the acoustic pressure field and RF radiofrequency signals of a homogeneous linear or convex probe. Results indicate that SIMUS can be used for a realistic simulation of medical ultrasound imaging. Kizielewicz et al. [23] utilized multi-criteria decision analysis (MCDA) methods, COMET and SPOTIS, respectively, to examine how obtained rankings vary. It was found that the SPOTIS methodology determines the ideal ISP solution requires specific boundaries, including a maximum boundary value for the profit criterion and a minimum boundary value for the cost criterion. While the COMET method exhibits good real-time performance, it occupies the connection for an extended period, losing stateless high concurrency and is often applied to stock systems as well as real-time communication. Więckowski et al. [24] proposed the RANkingl CoMparison (RANCOM) method, which can be used to determine criteria weights based on expert knowledge. Its performance relies on obtaining weight vectors based on the sorting order of the defined criteria. Arithmetic examples and a comparative analysis of the chosen subjective weighting methods demonstrate that the proposed method yields reliable results and exhibits a high correlation with existing methods. When comparing the RANCOM method with the AHPl method, it becomes evident that the RANCOM method achieves higher reproducibility, particularly when dealing with a high number of criteria. Maniya et al. [25,26,27] successfully addressed the problem of selecting houses using the ideal solution similarity ranking preference method (TOPSIS) in comparison with other methods. The results show that TOPSIS is suitable for accurate performance rating and can reflect the preferences well.

Comparing the mentioned methods, TOPSIS stands out due to several characteristics when contrasted with other computational methods. It employs logical thinking that articulates sound logic fundamental to human choice. Additionally, it provides a scalar value applicable to both the best and worst choices. The calculation process is straightforward and easily programmable into a spreadsheet. Given its ease of understanding and suitability for the specific topic, the TOPSIS method is chosen as the research method for this study.

All the above studies were focused on different types of building envelope and involved various theoretical calculation methods and evaluations of the energy-saving effects. Based on these studies, it can be stated that the energy-saving form of the building envelope will affect the heat transfer, the building load, and the indoor environment of the building. However, most of these studies were focused on the impact of one type of envelope system in relation to energy consumption or building environment without considering the overall impact of the envelope system as a whole, while the optimization of an energy-saving envelope system for a certain regional environment has not yet been addressed.

Considering these factors, the project “Tropical Monsoon Climate—Existing Single Residential Buildings in Wenzhou Region”, for instance, is a study that integrates the regional environment, resource conditions, energy consumption habits, and individual perceptual capabilities. With some consideration of the indoor thermal and humidity environment of the building, the envelope energy-saving system that is the most suitable for the single residential buildings in the region in question is selected with the aim of enhancing the energy-saving effect and meeting the indoor thermal and humidity environment needs of the residents, which will be of great significance to promoting the application and development of energy-saving technologies in tropical monsoon climate area, protecting the ecological environment, and driving regional economic development [28,29].

2. The Current Status of the Building Envelopes Typical of the Regional Climate

The global climate, classified by the Köppen climate system into A (tropical), B (dry), C (subtropical), D (temperate and subfreezing), and E (polar), places Wenzhou in category C. Additionally, the Schaller Climate Classification includes various climates like tropical rainforest, savannah, tropical desert, tropical monsoon, subtropical monsoon, Mediterranean, temperate oceanic, temperate monsoon, temperate continental, subarctic coniferous, polar, and highland mountain climates. Wenzhou falls under the tropical monsoon climate, which is characterized by high temperatures, rainy summers, and mild winters with little rain—similar to regions south of the Qinling Mountains in China, the North American and South American continents, and the southeastern part of the Australian continent. Hence, studying energy-saving envelope structures in Wenzhou is highly significant, providing insights for energy-efficient combinations in tropical monsoon climate areas and contributing to reducing the energy consumption of individual buildings.

Compared with typical urban residential buildings, the existing single residential buildings in Wenzhou area have their own distinct characteristics. In terms of design, the residential buildings in this area are mostly low-rise buildings, with a small size and a large shape coefficient, which is largely unconducive to energy saving [30,31,32,33]. The indoor thermal and wind environment of the residential buildings in this area tends to be poor in winter, and improving the thermal comfort and reducing the indoor wind speed in the winter months is thus a crucial task. Furthermore, various other issues are proving to be problematic, including serious cold air penetration into the residential buildings at night, the poor insulation performance of the envelope, the small difference between the indoor and outdoor temperatures, a lower indoor temperature at night than that of the thermal comfort zone, and poor thermal comfort caused by high relative humidity. In short, in terms of thermal environment evaluation, the environment is at its poorest during the winter, which is largely due to the inadequate building envelope design or the penetration of cold wind. Hence, the thermal insulation performance of the building envelope in the winter must be improved. In terms of indoor humidity evaluation, many residents in the area have reported that while indoor damping can occur, there are no corresponding moisture-proof methods in place to address the issue and improve the humidity adjustment performance of the building envelope of traditional dwellings [34,35].

This paper focuses on the different types of buildings existing in Wenzhou from 2012 to 2022. According to the statistics, the building types include office, education, commerce, sports, dormitory, medical care, and residency buildings. Meanwhile, the main building structures are based on a frame structure with shear walls. Here, the exterior wall materials include sintered porous bricks, non-clay sintered porous bricks, concrete porous bricks, coal gangue sintered porous bricks, cement porous bricks, waste soil sintered porous bricks, shale sintered porous bricks, cement standard bricks, cement hollow bricks, autoclaved lime–sand bricks, shale sintered hollow bricks, sintered shale bricks, shale modulus sintered bricks, composite ceramsite blocks, composite concrete blocks and small concrete blocks, double-row concrete hollow blocks, autoclaved aerated concrete blocks, composite ceramsite concrete blocks, concrete hollow blocks, light sand aerated concrete blocks, and ceramsite concrete hollow block, with the external wall thickness varying from between 180 and 360 mm. Meanwhile, the exterior wall’s thermal insulation materials mainly include rubber powder polystyrene particle thermal insulation slurry, polystyrene particle thermal insulation slurry, polymer thermal insulation mortar, sepiolite thermal insulation mortar, inorganic polymer thermal insulation mortar, inorganic lightweight aggregate polymer thermal insulation mortar, extruded polystyrene board, expanded polystyrene board, rigid foam polyurethane board, extruded polystyrene thermal insulation board, polystyrene particles, rubber powder polystyrene particles, rubber powder polystyrene particles, fiber glass wool, semi-hard glass wool board, semi-hard mineral (rock) cotton board, thermal insulation glass wool board (or rock wool and mineral wool board), thermal insulation rock wool felt, and mineral (rock) cotton or glass wool board, with the thickness of the insulation materials ranging from 12 to 55 mm. The main types of external window include PVC common hollow glass windows, PVC low-radiation hollow glass windows, insulated aluminum alloy low-radiation hollow glass windows, insulated aluminum alloy common hollow glass windows, insulated aluminum alloy low-radiation glass windows, ordinary aluminum alloy low-radiation hollow glass windows, multi-cavity insulated metal Low-e hollow glass windows, insulated metal low-radiation hollow glass windows, and plastic–steel common hollow glass windows. Meanwhile, the roof insulation materials can be categorized into extruded polystyrene foam plastic board, polystyrene particle insulation mortar, hard foam polyurethane board, foam concrete, STP insulation board, polystyrene color steel sandwich board, mineral (rock) cotton, glass wool board, foam glass insulation board, microporous calcium silicate board, hard rock cotton board, semi-hard glass wool board, and semi-hard mineral (rock) cotton board with the thickness of the materials ranging from 20 to 125 mm.

3. Initial Combination Forms of Energy-Saving Envelope

Based on the pattern of compartment and intermittent energy consumption in Wenzhou area, the main focus of the energy-saving design of the building envelope in this area is improving the overall performance of the envelope (roof, external walls, external doors, external windows, etc.) with the aim of maintaining a stable and comfortable indoor environment and reducing the cold and heat load of the air-conditioning equipment [36]. The energy-saving building envelope largely involves the external walls, the roof, the external windows, and the specific shading design, as shown in

Table 1. Initial combination forms of building envelope (variable is wall material).

Forms of Combination	The Wall Materials	The Roof Materials	The Window Materials	The Shading Methods
Series 1	Ceramsite concrete composite block	Basic Roof	Basic Window	No Shading
Series 2	Sintered porous brick
Series 3	Sintered hollow brick
Series 4	Autoclaved sand aerated concrete block
Series 5	Autoclaved fly ash aerated concrete block
Series 6	Foam concrete block
Series 7	Non-clay sintered insulation brick
Series 8	Lightweight aggregate concrete hollow
Series 9	Ordinary concrete porous brick
Series 10	Ordinary concrete porous block
Series 11	Sintered ordinary brick masonry
Series 12	Non-clay sintered insulation block

,

Table 2. Initial combination forms of building envelope (variable is roof material).

Forms of Combination	The Roof Materials		The Wall Materials	The Window Materials	The Shading Methods
Series 13	Plant height	0.10	Basic Wall	Basic Window	No Shading
Series 14		0.20
Series 15		0.30
Series 16		0.40
Series 17		0.50
Series 18	Leaf area index	0.10
Series 19		0.20
Series 20		0.30
Series 21		0.40
Series 22		0.50
Series 23		1.00
Series 24	Leaf reflectivity	0.10
Series 25		0.20
Series 26		0.30
Series 27		0.40
Series 28		0.80
Series 29		0.85
Series 30		0.90
Series 31		0.95
Series 32	Leaf roughness	very rough
Series 33		medium rough
Series 34		rough
Series 35		smooth
Series 36		medium smooth
Series 37		very smooth
Series 38	Soil thickness	0.05
Series 39		0.10
Series 40		0.20
Series 41		0.30
Series 42		0.40
Series 43		0.50
Series 44	Soil conductivity	0.40
Series 45		0.50
Series 46		0.60
Series 47		0.70
Series 48		0.80
Series 49		0.90
Series 50		1.00
Series 51	Soil specific heat	500
Series 52		1000
Series 53		1500
Series 54		2000
Series 55	Soil density	300
Series 56		400
Series 57		500
Series 58		1000
Series 59		1500
Series 60		2000

,

Table 3. Initial combination forms of building envelope (variable is window material).

Forms of Combination	The Window Materials	The Wall Materials	The Roof Materials	The Shading Methods
Series 61	u = 6.5 plastic–steel window + standard external window	Basic Wall	Basic Roof	No Shading
Series 62	u = 6.5 plastic–steel window + blue heat-absorbing single glass
Series 63	u = 6.5 plastic–steel window + high-transmittance heat-reflecting glass
Series 64	u =4 plastic–steel window + ordinary hollow
Series 65	u = 4.6 plastic–steel window + low–e medium transmittance
Series 66	u = 4.7 paternal steel window + low–e high transmittance
Series 67	u = 6.6 model steel window + ordinary 3-mm single glass
Series 68	u = 5.7 insulated heat aluminum alloy + standard external window
Series 69	u = 5.7 insulated aluminum alloy + blue heat-absorbing glass single glass
Series 70	u = 5.7 insulated aluminum alloy + high-transmittance heat-reflecting glass
Series 71	u = 3.4 insulated aluminum alloy + ordinary hollow
Series 72	u = 4 insulated aluminum alloy + low–e medium permeability
Series 73	u = 4 insulated aluminum alloy + low–e high permeability
Series 74	u = 5.8 insulated aluminum alloy + ordinary 3 mm single glass
Series 75	u = 4.9 PVC plastic–steel type + standard external window
Series 76	u = 4.9 PVC plastic–steel type + blue heat-absorbing glass single glass
Series 77	u = 4.9 PVC plastic–steel type + high-transmitting heat-reflecting glass
Series 78	u = 2.8 PVC plastic–steel type + ordinary hollow
Series 79	u = 3.3 PVC plastic–steel type + low–e medium permeability
Series 80	u = 3.4 PVC plastic–steel type + low–e high permeability
Series 81	u = 5 PVC plastic–steel type + ordinary 3–mm single glass

and

Table 4. Initial combination forms of building envelope (variable is shading methods).

Forms of Combination	The Shading Methods			The Wall Materials	The Roof Materials	The Window Materials
Series 82	Horizontal shading	Transmittance	0.20	Basic Wall	Basic Roof	Basic Window
Series 83			0.30
Series 84			0.40
Series 85			0.50
Series 86			0.70
Series 87			0.80
Series 88		Extension length of the sun visor	100
Series 89			200
Series 90			300
Series 91			400
Series 92			500
Series 93			600
Series 94			700
Series 95			800
Series 96			900
Series 97			1000
Series 98			1100
Series 99			1200
Series 100	Vertical shading	Transmittance	0.15
Series 101			0.30
Series 102			0.40
Series 103			0.50
Series 104			0.70
Series 105			0.80
Series 106		Extension length of the sun visor	100
Series 107			200
Series 108			300
Series 109			400
Series 110			500
Series 111			600
Series 112			700
Series 113			800
Series 114			900
Series 115			1000
Series 116			1100
Series 117			1200
Series 118	Comprehensive shading	Transmittance	0.15
Series 119			0.30
Series 120			0.40
Series 121			0.50
Series 122			0.70
Series 123			0.80
Series 124		Extension length of the sun visor	100
Series 125			200
Series 126			300
Series 127			400
Series 128			500
Series 129			600
Series 130			700
Series 131			800
Series 132			900
Series 133			1000
Series 134			1100
Series 135			1200
Series 136	Louver-based shading	Transmittance	0.15
Series 137			0.30
Series 138			0.40
Series 139			0.50
Series 140			0.70
Series 141			0.80
Series 142		Louver angle	0°
Series 143			10°
Series 144			20°
Series 145			30°
Series 146			40°
Series 147			50°
Series 148			60°
Series 149			70°
Series 150			80°
Series 151			90°
Series 152			100°
Series 153			110°
Series 154			120°
Series 155			130°
Series 156			140°
Series 157			150°
Series 158			160°
Series 159			170°
Series 160			180°

.

Here, the different wall materials can be divided into 12 types: ceramsite concrete composite block, sintered porous brick, sintered hollow brick, autoclaved sand aerated concrete block, autoclaved fly ash aerated concrete block, foam concrete block, non-clay sintered insulation brick, lightweight aggregate concrete hollow block, ordinary concrete porous brick, ordinary concrete porous block, sintered ordinary brick masonry, and non-clay sintered insulation block. Meanwhile, there are 48 roof variables, including plant height, leaf area index, leaf reflectivity, roughness, and thickness, as well as soil conductivity and soil-specific heat. In terms of the external windows, these can be divided into 21 types, which includes different frames (e.g., plastic–steel windows, heat-insulating aluminum alloy) and different glasses (e.g., blue heat-absorbing single glass, high-transmittance heat-reflecting glass, Low-e medium transmission and Low-e high transmission glass). Finally, the shading designs include horizontal shading, vertical shading, comprehensive shading, and louver-based shading, with the attendant variables divided into 79 types, including transmittance, the extension length of the sun visor, and the louver angle.

4. Regional Environment Characteristics

As the research target is the buildings in the south of Zhejiang Province, Wenzhou City, a typical representative city in this region, was chosen as the research object. Wenzhou City lies within a subtropical monsoon climate zone with significant alternating winter and summer monsoons, moderate temperatures, four distinct seasons, and abundant rainfall. Here, the winters are not particularly severe, while the summers are not blisteringly hot. Due to the usage of air conditioners, fans, or other thermal resources for indoor environment adjustment, the transitional periods, i.e., March, April, May, September, October, and November, were selected for the test cycle. Since the residents rarely require any intervention in terms of the indoor environment in the transitional periods, the influence of the energy-saving building envelope on the indoor environment and the energy consumption could be accurately analyzed. The environmental data were collected in real time during the research period with the collection results shown in Figure 1 and the outdoor environmental data summarized in

Table 5. Average outdoor environmental data during the test period.

	Outdoor Air Temperature (°C)	Outdoor Air Relative Humidity (%)	Environment Wind Speed (m/s)	Solar Radiation Rate (W/m2)
March	11.93	79.00	1.39	140.98
April	16.93	79.48	1.55	154.38
May	20.97	84.23	1.50	176.77
September	25.51	79.13	2.10	170.31
October	20.86	76.01	1.65	155.59
November	15.90	71.06	1.39	127.68

.

As Table 5 shows, during the target period, the average value of the outdoor air relative humidity in March is 79.00%, the average value of the outdoor ambient wind speed is 1.39 m/s, the average value of the total solar radiation is 140.98 W/m², and the average value of the outdoor air temperature is 11.93 °C. In April, the average relative humidity of the outdoor air is 79.48%, the average wind speed of the outdoor environment is 1.55 m/s, the average total solar radiation is 154.38 W/m², and the average outdoor air temperature is 16.93 °C. In May, the average relative humidity of the outdoor air is 84.23%, the average wind speed of the outdoor environment is 1.50 m/s, the average total solar radiation is 176.77 W/m², and the average outdoor air temperature is 120.97 °C. In September, the average relative humidity of the outdoor air is 79.13%, the average wind speed of outdoor environment is 2.10 m/s, the average total solar radiation is 170.31 W/m², and the average outdoor air temperature is 25.51 °C. In October, the average relative humidity of the outdoor air is 76.01%, the average wind speed of the outdoor environment is 1.65 m/s, the average total solar radiation is 155.59 W/m², and the average outdoor air temperature is 20.86 °C. In November, the average relative humidity of the outdoor air is 71.06%, the average wind speed of the outdoor environment is 1.39 m/s, the average total solar radiation is 127.68 W/m², and the average outdoor air temperature is 5.90 °C.

5. Single Residential Buildings and Energy Consumption Pattern of the Indoor Personnel

5.1. Single Residential Buildings

A typical single residential building in Wenzhou City was selected as the research object. The building orientation is 398° (−62°), the building size is 9000 mm × 7800 mm × 3700 mm, the window size is 3800 mm × 1460 mm, the window height is 1000 mm, the door size is 1240 mm × 2960 mm, the distance from the western wall is 500 mm, and the indoor area measures 70.20 m². Figure 2 and Figure 3 show the plan view and the isometric view of the single residential building, respectively.

The foundation building envelope of the building is composed of an inverted roof, reinforced concrete walls, marble floors, standard single-layer glass windows, and all-wood doors. The details of the structure are listed in

Table 6. Details of building envelope structure.

Structure	Practice
Roof (inverted)	From top to bottom: 70 mm thick cement cinder slope layer, 20 mm thick cement mortar leveling layer, 10 mm thick modified asphalt flexible linoleum waterproof layer, 130 mm thick extruded polystyrene board, 20 mm thick cement mortar protective layer, 100 mm thick reinforced concrete, and 20 mm thick cement mortar plastering.
Walls	From the outside to the inside: 20 mm thick cement mortar plastering, 200 mm thick reinforced concrete plastering, and 20 mm thick cement mortar plastering.
Floors	From top to bottom: 12 mm thick marble floor tile, 100 mm thick reinforced concrete.
All-wood door	A 25 mm thick pine and spruce wooden door.

, while the thermal parameters of the structure are shown in

Table 7. Detail of the building envelope.

Envelopes	Thermal Conduction Resistance ((m·K)/W)	Heat Transfer Coefficient (W/(m2·K))	Thermal Inertness Index
Wall	0.16	3.16	2.64
Floor	0.06	4.55	2.86
Roof (inverted)	4.39	0.22	2.84
All-wood door	0.18	2.95	—

.

As Table 6 and Table 7 show, the thermal parameters of the envelope structure all meet the requirements of the Design Standards for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Areas [37].

5.2. Energy Consumption Pattern of the Indoor Personnel

To compare the energy-saving effects of different building envelopes, the behavioral habits of the indoor personnel were set as identical according to the energy use habits of residents in Wenzhou [38]. The lighting equipment in the room is an incandescent lamp with the minimum illumination set at 300 lx. Here, the convection part accounts for 20% of the heat disturbance distribution of the incandescent lamp, including 40% of the surrounding wall surface, 50% of the floor, and 10% of the ceiling.

Table 8. Behavior of the people in the room.

Pattern				Time Quantum
Natural ventilation	Workday	Open the door	May, September	06:00–08:00, 17:00–19:00
		Open the window	March, April, May, September, October, November	06:00–08:00, 17:00–19:00
	Day off	Open the door	May, September	08:00–19:00
		Open the window	March, April, May, September, October, November	08:00–19:00
Illumination	Turn on the light			06:00–08:00, 17:00–24:00

shows the specific behavioral habits of the indoor personnel.

As the table shows, during the transitional periods, the indoor personnel were set to leave at 8:00 a.m. and to arrive home at 17:00 on weekdays and to stay at home all day on their days off. Based on the typical energy consumption behavior of Wenzhou residents, the energy consumption patterns of the experimental buildings were divided into two patterns: working days and rest days. In terms of working days, the time periods for opening the doors and windows for ventilation in May and September were set at 06:00 to 08:00 and 17:00 to 19:00, while the time period for opening the windows for ventilation in March, April, October, and November was set at 06:00 to 08:00. According to the relevant historical meteorological data, the sunrise time during the transitional periods ranges from 5:16 to 6:30, while the sunset time ranges from 17:12 to 18:48. Therefore, the indoor lighting time was set at 06:00 to 08:00 and 17:00 to 24:00 to ensure sufficient brightness in the rooms.

6. Calculation Method

6.1. TOPSIS Basic Principles

The Technique for Order Preference by Similarity to an Ideal Solution algorithm (TOPSIS) is a technology employed for determining the ideal solution through a multi-objective decision-making method. This methodology assists decision-makers in analyzing, comparing, and ranking available options when multiple criteria are considered. The fundamental concept of TOPSIS is straightforward, originating from the idea of a displaced ideal point where the solution with the shortest distance is identified [39]. Hwang et al. [40] extended this concept, suggesting that the ranking of alternatives is based on the shortest distance from the ideal solution or positive ideal solution (PIS) and the farthest distance from the negative-ideal solution (NIS), anti-ideal solution, or nadir in an n-dimensional Euclidean space. TOPSIS takes into account both distances to PIS and NIS simultaneously, establishing a preference order based on their relative closeness, which is a combination of these two distance measures.

In this paper, the envelope is segmented into four components: walls, roof, external windows, and shading aspects. Each component is then sub-divided into various types, which are characterized by low-quality indexes and high-quality indexes. The low-quality indexes encompass parameters like indoor and outdoor temperature differences, roof-surface thermal conductivity, and heat loss. These low-quality indexes need to be converted before comprehensive evaluation alongside the high-quality indexes. Thus, the TOPSIS method is deemed suitable in this context for selecting an optimal combination form for each part of the envelope.

6.2. TOPSIS Calculation Steps

1.

Create a decision or evaluation matrix D

The matrix consists of m alternatives (A₁, …, A_m) and n criteria (X₁, …, X_n) with its element X_ij, where i = 1, …, m and j = 1, …, n.

$$\begin{array}{l}D={\begin{array}{cc}\begin{array}{c}\\ A_1\\ A_2\\ ⋮\\ A_{\mathrm{i}}\\ ⋮\\ A_{\mathrm{m}}\end{array}& \begin{array}{c}\begin{array}{cccccc}{X}_1& X_2& \cdots & X_j& \cdots & X_n\end{array}\\ \left[\begin{array}{cccccc}{x}_{11}& x_{12}& \cdots & x_{1j}& \cdots & x_{1n}\\ x_{21}& x_{22}& \cdots & x_{2j}& \cdots & x_{2n}\\ ⋮& ⋮& \cdots & ⋮& \cdots & ⋮\\ x_{i1}& x_{i2}& \cdots & x_{ij}& \cdots & x_{in}\\ ⋮& ⋮& \cdots & ⋮& \cdots & ⋮\\ x_{m1}& x_{m2}& \cdots & x_{mj}& \cdots & x_{mn}\end{array}\right]\end{array}\end{array}}_{m\times n}\\ \end{array}$$

(1)

2.

Construct the normalized decision matrix R

Matrix D is normalized to matrix R with m alternatives and n criteria, and its element r_ij, $r{i}_j=\frac{x_{ij}}{\sqrt{{\displaystyle {\sum }_{i=1}^m{x}_{ij}^2}}}$ where i = 1, …, m and j = 1, …, n.

$$\begin{array}{l}R={\begin{array}{cc}\begin{array}{c}\\ A_1\\ A_2\\ ⋮\\ A_{\mathrm{i}}\\ ⋮\\ A_{\mathrm{m}}\end{array}& \begin{array}{c}\begin{array}{cccccc}{X}_1& X_2& \cdots & X_j& \cdots & X_n\end{array}\\ \left[\begin{array}{cccccc}{r}_{11}& r_{12}& \cdots & r_{1j}& \cdots & r_{1n}\\ r_{21}& r_{22}& \cdots & r_{2j}& \cdots & r_{2n}\\ ⋮& ⋮& \cdots & ⋮& \cdots & ⋮\\ r_{i1}& r_{i2}& \cdots & r_{ij}& \cdots & r_{in}\\ ⋮& ⋮& \cdots & ⋮& \cdots & ⋮\\ r_{m1}& r_{m2}& \cdots & r_{mj}& \cdots & r_{mn}\end{array}\right]\end{array}\end{array}}_{m\times n}\\ \end{array}$$

(2)

3.

Construct the weighted normalized decision matrix V

A set of weights w = (w₁, …, w_n), and ${\displaystyle {\sum }_{j=1}^n{w}_j=1}$, where w_j > 0, j = 1, …, n is given to the corresponding criterion X_j, where j = 1, …, n. The matrix V = [w_jr_ij] is calculated by multiplying the elements at each column of the matrix R by their associated weights w_j, j = 1, …, n.

$$\begin{array}{l}V={\begin{array}{cc}\begin{array}{c}\\ A_1\\ A_2\\ ⋮\\ A_{\mathrm{i}}\\ ⋮\\ A_{\mathrm{m}}\end{array}& \begin{array}{c}\begin{array}{cccccc}{X}_1& X_2& \cdots & X_j& \cdots & X_n\end{array}\\ \left[\begin{array}{cccccc}{v}_{11}& v_{12}& \cdots & v_{1j}& \cdots & v_{1n}\\ v_{21}& v_{22}& \cdots & v_{2j}& \cdots & v_{2n}\\ ⋮& ⋮& \cdots & ⋮& \cdots & ⋮\\ v_{i1}& v_{i2}& \cdots & v_{ij}& \cdots & v_{in}\\ ⋮& ⋮& \cdots & ⋮& \cdots & ⋮\\ v_{m1}& v_{m2}& \cdots & v_{mj}& \cdots & v_{mn}\end{array}\right]\end{array}\end{array}}_{m\times n}\\ \end{array}$$

(3)

4.

Determine the positive ideal and negative-ideal solutions Z⁺ (PIS) and Z⁻ (NIS)

$$Z^{+}=(Z_1^{+}, Z_2^{+}, \dots , Z_m^{+})=\left\{\underset{i}{\max }{Z}_{ij}/j=1, 2, \dots , m\right\}$$

(4)

$$Z^{-}=(Z_1^{-}, Z_2^{-}, \dots , Z_m^{-})=\left\{\underset{i}{\min }{Z}_{ij}/j=1, 2, \dots , m\right\}$$

(5)

respectively, where J is associated with the benefit criteria and J’ is associated with the cost criteria, on criterion X_j, where j = 1, …, n for all alternatives A_i, i = 1, …, m.

5.

Distance measurement

Assuming that the decision problem involves m objectives f_j (j = 1, 2, …, m) and n feasible solutions Z_i = (Z_i1, Z_i2 …, Z_im) (i = 1, 2, … n), and that the ideal solution of the normalized weighted objective of this problem is Z⁺, where $Z^{+}=(Z_1^{+}, Z_2^{+}, \dots , Z_m^{+})$, the distance from any feasible solution Z_i to Z⁺ when using the Euclidean norm as the measure of distance can be described as follows:

$$S_i^{+}=\sqrt{{\displaystyle {\sum }_{j=1}^m{\left(Z_{ij}-Z_j^{+}\right)}^2}}, i=1, 2, \dots , n$$

(6)

where Z_ij is the normalized weighted value of the jth target to the ith scheme (solution).

In the same way, if this is set as the negative ideal solution of the normalized weighted objective of the problem, any feasible solution can be obtained.

Assuming that the distance between Z_i and the negative ideal solution Z⁻ is as follows:

$$S_i^{-}=\sqrt{{\displaystyle {\sum }_{j=1}^m{\left(Z_{ij}-Z_j^{-}\right)}^2}}, i=1, 2, \dots , n$$

(7)

the relative proximity of a feasible solution to an ideal solution can be defined as follows:

$$C_i=\begin{array}{cc}\frac{S_i^{-}}{S_i^{-}+S_i^{+}}& 0\le \end{array}{C}_i\le 1, i=1, 2, \dots , n$$

(8)

Therefore, if Z_i is an ideal solution, the corresponding C_i = 1, while if Z_i is a negative ideal solution, the corresponding C_i = 0. The closer Z_i is to the ideal solution, the closer C_i is to 1, while the closer C_i is to the negative ideal solution, the closer it is to 0. Then, C_i can then be sorted to determine a satisfactory solution.

6.3. RANCOM Basic Principles

The RANCOM method introduces a novel approach to determining criterion relevance through subjective evaluations, allowing for the handling of inaccuracies in expert judgment. This methodology employs an intuitive approach to assess standard relationships and establish rankings, catering to both less and more experienced users, thereby solving complex problems efficiently. Key features include providing significant weight diversity in problems with multiple criteria, ensuring results closely align with existing methods, addressing inaccuracies in expert judgment, and displaying high reproducibility. Additionally, the method addresses shortcomings in currently used methods, streamlining the subjective identification process of criteria importance by experts.

In alignment with the TOPSIS findings mentioned earlier, Series 1~Series 12 variables represent different exterior wall materials, Series 13~Series 60 variables denote various planted roofs, Series 61~Series 81 correspond to different window materials, and Series 82~Series 160 encompass distinct shading materials. The decision matrix for the selected criteria is formed by choosing the first four optimal solutions from each of the four material categories. Subsequently, the RANCOM method is employed for importance analysis of the parameters.

6.4. RANCOM Calculation Steps

1.

Define the criteria ranking

The expert determines the position of the criteria regarding other factors. The designated ranking should be defined as lower values assigned to more significant criteria. Additionally, the criteria may have equal positions in the ranking, which means that ties are allowed during the expert judgment. The criteria ranking could be defined with subsequent values (i.e., 1–5 for five criteria) or could consist of more diverse values (i.e., 1, 5, 9, 12, and 18 for five criteria). However, the differences that occurred in the ranking vector would not affect the calculated weights unless they include different criteria hierarchy.

2.

Establish the Matrix of Ranking Comparison

The MAtrix of ranking Comparison (MAC) is determined by using a pairwise comparison of the positions from the ranking made by the expert. The comparison result is determined as α_ij. Based on that, the MAC matrix can be represented as (9):

$$MAC=\begin{array}{cc}& \begin{array}{cccc}{C}_1& C_2& \cdots & C_{\mathrm{n}}\end{array}\\ \begin{array}{c}{C}_1\\ C_2\\ ⋮\\ C_n\end{array}& \left[\begin{array}{cccc}{\alpha }_{11}& {\alpha }_{12}& \cdots & {\alpha }_{1n}\\ {\alpha }_{21}& {\alpha }_{22}& \cdots & {\alpha }_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ {\alpha }_{n1}& {\alpha }_{n2}& \cdots & {\alpha }_{nn}\end{array}\right]\end{array}$$

(9)

where n is the number of criteria taken into account in the problem, and α_ij is determined from (10):

$${\alpha }_{ij}=\left\{\begin{array}{l}\begin{array}{cccc}IF& f(C_i)<f(C_j)& THEN& 1\end{array}\\ \begin{array}{cccc}IF& f(C_i)=f(C_j)& THEN& 0.5\end{array}\\ \begin{array}{cccc}IF& f(C_i)>f(C_j)& THEN& 0\end{array}\end{array}\right.$$

(10)

where ƒ(C) is a significance function of criterion C.

3.

Calculate the Summed Criteria Weights

Based on the obtained MAC, the horizontal vector of the Summed Criteria Weights (SCWs) is obtained as follows (11).

$$SW{C}_i={\displaystyle \sum _{j=1}^n{\alpha }_{ij}}$$

(11)

4.

Calculate the final criteria weights

Finally, values of preference are approximated for each criterion. As a result, the horizontal vector W is obtained, where the ith row contains the approximate preference value for C_i. The weights for the set of criteria are obtained as (12):

$$W_i=\frac{SC{W}_i}{{\displaystyle {\sum }_{i=1}^{n}SC{W}_i}}$$

(12)

7. Results and Analysis

7.1. TOPSIS Results

The building environment under different envelope combinations was simulated using ENERGYPLUS 9.5 software to assess the following: the wall-surface temperature, the indoor temperature and the indoor and outdoor temperature difference under wall series 1 to series 12, the roof-surface temperature and thermal conductivity under different plant heights, the leaf area index, the leaf reflectivity, the soil roughness, the soil thickness, the soil conductivity, the soil-specific heat, and the soil density in series 13 to series 60 of the planting roofs, the window heat increase rate, the window heat loss rate, the regional air temperature and the outer window inner-surface temperature under window series 61 to series 81, the incident solar emissivity, the window heat growth rate, the window heat loss rate and inner-surface temperature under different transmittance and shade heights when the shading methods are horizontal shading, vertical shading, and comprehensive shading in the sunshade series 82 to series 160, and the incident solar emissivity, window heat growth rate, window heat loss rate and inner-surface temperature under different louver angles when the shading method is louver shading. The above were assessed using the TOPSIS algorithm with the calculated results compared and sorted. The results are shown in Figure 4.

The results, which are shown in Figure 4, were as follows:

1.

Walls

The Ci values were arranged in the order from large to small as C₃ > C₉ > C₈ > C₇ > C₂ > C₁₀ > C₁ > C₁₂ > C₄ > C₅ > C₁₁ > C₆. In other words, we identified the following descending order in terms of approaching the ideal solution: sintered hollow brick > common concrete porous brick > lightweight aggregate concrete hollow block > non-clay sintered insulation brick > sintered porous brick > common concrete porous block > ceramsite concrete composite block > non-clay sintered insulation block > autoclaved sand aerated concrete block > autoclaved fly ash aerated concrete block > sintered common brick masonry > foam concrete block.

2.

Roof

Here, there were numerous influencing factors, which could be divided into eight main categories: plant height, leaf area index, leaf reflectivity, soil roughness, soil thickness, soil conductivity, soil-specific heat, and soil density. These eight factors were calculated together but will be discussed separately. Here, the following was concluded: (i) when the plant height was the variable, the order of the C_i value from large to small was C₁₄ > C₁₅ > C₁₆ > C₁₃, that is, the descending order in terms of approaching the ideal solution was 0.2 > 0.3 > 0.4 > 0.1 > 0.5 m; (ii) when the leaf area index was the variable, the order of the C_i values from large to small was C₂₃ > C₂₂ > C₂₁ > C₂₀ > C₁₉ > C₁₈, that is, the descending order in terms of approaching the ideal solution was 1 > 0.5 > 0.4 > 0.3 > 0.2 > 0.1; (iii) when the leaf reflectivity was the variable, the order of the C_i values from large to small was C₂₈ > C₂₅ > C₂₉ > C₂₄ = C₃₁ > C₃₀ > C₂₆ > C₂₇, that is, the descending order in terms of approaching the ideal solution was 0.8 > 0.2 > 0.85 > 0.1 > 0.95 > 0.9 > 0.3 > 0.4; (iv) when the soil roughness was the variable, the order of the C_i values from large to small was C₃₅ > C₃₆ > C₃₇ > C₃₃ > C₃₄ > C₃₂, that is, the descending order in terms of approaching the ideal solution was smooth > medium smooth > very smooth > medium rough > rough > very rough; (v) when the soil thickness was the variable, the order of the C_i values from large to small was C₃₈ > C₃₉ > C₄₀ > C₄₃ > C₄₁ > C₄₂, that is, the descending order in terms of approaching the ideal solution was 0.05 > 0.1 > 0.2 > 0.5 > 0.3 > 0.4 m; (vi) when soil conductivity was the variable, the order of the C_i values from large to small was C₄₇ > C₄₈ > C₄₄ > C₅₀ > C₄₉ > C₄₅ > C₄₆, that is, the descending order in terms of approaching the ideal solution was 0.7 > 0.8 > 0.4 > 1.0 > 0.9 > 0.5 > 0.6; (vii) when soil-specific heat was the variable, the order of the Ci values from large to small was C₅₃ > C₅₁ > C₅₂ > C₅₄, that is, the descending order in terms of approaching the ideal solution was 1500 > 500 > 1000 > 2000 j/(kg·k); and (viii) when soil density was the variable, the order of the C_i values from large to small was C₆₀ > C₅₉ > C₅₇ > C₅₈ > C₅₆ > C₅₅, that is, the descending order in terms of approaching the ideal solution was 2000 > 1500 > 500 > 1000 > 400 > 300 kg/m³.

3.

Windows

The order of the C_i values from large to small was C₇₈ > C₇₉ > C₈₀ > C₇₁ > C₇₂ = C₇₃ > C₆₄ > C₆₅ > C₇₅ = C₇₆ = C₇₇ > C₈₁ > C₆₈ = C₆₉ = C₇₀ > C₇₄ > C₆₁ = C₆₂ = C₆₃ > C₆₇ > C₆₆. In other words, the descending order in terms of approaching the ideal solution was u = 2.8 PVC plastic–steel type + ordinary hollow, u = 3.3 PVC plastic–steel type + Low-e medium permeability, u = 3.4 PVC plastic–steel type + Low-e high permeability, u = 3.4 insulated aluminum alloy + ordinary hollow, u = 4 insulated aluminum alloy + Low-e medium permeability, u = 4 insulated aluminum alloy + Low-e high permeability, u = 4 plastic–steel window + ordinary hollow, u = 4.6 plastic–steel window + Low-e medium transmittance, u = 4.9 PVC plastic–steel type + standard external window, u = 4.9 PVC plastic–steel type + blue heat-absorbing glass single glass, u = 4.9 PVC plastic–steel type + high-transmittance heat-reflecting glass, u = 5 PVC plastic–steel type + ordinary 3 mm single glass, u = 5.7 insulated heat aluminum alloy + standard external window, u = 5.7 insulated aluminum alloy + blue heat-absorbing glass single glass, u = 5.7 insulated aluminum alloy + high-transmittance heat-reflecting glass, u = 5.8 insulated aluminum alloy + ordinary 3 mm single glass, u = 6.5 plastic–steel window + standard external window, u = 6.5 plastic–steel window + blue heat-absorbing single glass, u = 6.5 plastic–steel window + high-transmittance heat-reflecting glass, u = 6.6 model steel window + ordinary 3 mm single glass, u = 4.7 paternal steel window + Low-e high transmittance.

4.

Shading

As noted, the shading was divided into four categories: horizontal shading, vertical shading, comprehensive shading, and louver shading. The results were as follows. In terms of horizontal shading, when the transmittance was the variable, the order of the C_i values from large to small was C₈₂ > C₈₇ > C₈₆ > C₈₅ > C₈₄ > C₈₃; that is, the descending order in terms of approaching the ideal solution was 0.15 > 0.8 > 0.7 > 0.5 > 0.4 > 0.3. When the extension length of the sun visor was selected as the variable, the order of the Ci values from large to small was C₈₈ > C₈₉ > C₉₀ > C₉₁ > C₉₂ > C₉₃ > C₉₉ > C₉₄ > C₉₅ > C₉₆ > C₉₇ > C₉₈ > C₉₉; that is, the descending order in terms of approaching the ideal solution was 1000 > 200 > 300 > 400 > 500 mm. In terms of vertical shading, when the transmittance was the variable, the order of the C_i values from large to small was C₁₀₅ > C₁₀₄ > C₁₀₃ > C₁₀₂ > C₁₀₁ > C₁₀₀; that is, the descending order in terms of approaching the ideal solution was 0.8 > 0.7 > 0.5 > 0.4 > 0.3 > 0.15. When the length of the sun visor was selected as the variable, the order of the C_i values from large to small was C₁₀₆ > C₁₀₇ > C₁₀₈ > C₁₀₉ > C₁₁₀ > C₁₁₁ > C₁₁₂ > C₁₁₃ > C₁₁₄ > C₁₁₅ > C₁₁₆ > C₁₁₇ with the corresponding values in terms of approaching the ideal solution 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, and 1200 mm. In terms of comprehensive shading, when the transmittance was the variable, the order of the C_i values from large to small was C₁₂₃ > C₁₂₂ > C₁₂₁ > C₁₂₀ > C₁₁₉ > C₁₁₈; that is, the descending order in terms of approaching the ideal solution was 0.8 > 0.7 > 0.5 > 0.4 > 0.3 > 0.15. When the length of the sun visor was selected as the variable, the order of the C_i values from large to small was C₁₂₄ > C₁₂₅ > C₁₂₆ > C₁₂₇ > C₁₂₈ > C₁₂₉ > C₁₃₀ > C₁₃₁ > C₁₃₂ > C₁₃₃ > C₁₃₄ > C₁₃₅; that is, the descending order in terms of approaching the ideal solution was 100 > 200 > 300 > 400 mm. In terms of louver shading, when the transmittance was the variable, the order of the C_i values from large to small was C₁₄₁ > C₁₄₀ > C₁₃₉ > C₁₃₈ > C₁₃₇ > C₁₃₆; that is, the descending order in terms of approaching the ideal solution was 0.8 > 0.7 > 0.5 > 0.4 > 0.3 > 0.15. When the louver angle was the variable, the order of the C_i values from large to small was C₁₅₈ > C₁₅₇ > C₁₅₉ > C₁₄₂ > C₁₆₀ > C₁₅₆ > C₁₄₃ > C₁₅₅ > C₁₄₄ > C₁₅₄ > C₁₄₅ > C₁₄₆ > C₁₅₃ > C₁₄₇ > C₁₅₂ > C₁₄₈ > C₁₅₁ > C₁₄₉ > C₁₅₀ with the corresponding values in terms of approaching the ideal solution 160°, 150°, 170°, 0°, 40°, 180°, 140°, 10°, 130°, 20°, 120°, 30°, 110°, 50°, 100°, 60°, 90°, 70°, and 80°.

According to the above calculation results, among the different forms of the building envelope, the one that is closest to the ideal solution can be achieved when the wall material is sintered hollow brick, and when the height of the plants on the planting roof is 0.2 m, the leaf area index is 1, the leaf reflectivity is 0.8, the roughness is smooth, the thickness is 0.05 m, the soil conductivity is 0.7, the soil-specific heat is 1500 J/kg °C, and the soil density is 2000 kg/m³. When the window type is u = 2.8 PVC plastic–steel type + ordinary hollow, this is the closest to the ideal solution. Meanwhile, in terms of horizontal shading design, this is the closest to the ideal solution when the transmittance is 0.15 and the extension length of the sun visor is 100 mm, while in terms of the vertical shading design, the transmittance should be 0.8 and the extension length should be 100 mm, and in terms of louver shading, the transmittance should be 0.8 and the louver angle should be 160°. In sum, the energy-saving building envelopes are defined in terms of four categories, as shown in Figure 5.

7.2. RANCOM Results

The first four optimal solutions were selected among different exterior wall materials, various planted roofs, distinct window materials, and diverse shading methods to form the decision matrix of the selected criteria. The importance analysis was then conducted using the RANCOM method with the parameters shown in

Table 9. Decision matrix for the selected criteria.

	C1	C2	C3	C4
The wall materials	Sintered hollow brick	Ordinary concrete porous brick	Lightweight aggregate concrete hollow	Non-clay sintered insulation brick
The roof materials	Soil thickness 0.05	Soil density 1500	Plant height 0.20	Leaf roughness smooth
The window materials	u = 2.8 PVC plastic–steel type + ordinary hollow	u = 3.3 PVC plastic–steel type + Low–e medium permeability	u = 3.4 PVC plastic–steel type + Low–e high permeability	u = 3.4 insulated aluminum alloy + ordinary hollow
The shading materials	Horizontal shading transmittance 0.20	Vertical shading transmittance 0.80	Comprehensive shading–extension length of the sun visor–200	Louver–based shading transmittance 0.80

.

The RANCOM algorithm was employed for evaluation, and the results of parameter importance weight calculations were compared and ranked. The results are as follows:

$$W=\left[\begin{array}{c}0.2222\\ 0.5556\\ 0.1667\\ 0.0556\end{array}\right]$$

Ultimately, among the four variables—exterior wall materials, planted roofs, window materials, and shading forms—the weights, in descending order, are window materials, exterior wall materials, planted roofs, and shading forms in the process of influencing building energy consumption. These results align with those obtained using the TOPSIS method.

7.3. Optimal Energy Saving Form Energy Consumption for Single Building

The energy consumption of the four energy-saving buildings shown in Figure 5 was simulated according to the energy consumption pattern presented in Table 5, with the building loads of the energy-saving buildings compared with those of the basic building. The results are shown in Figure 6. After comparing the exterior wall temperature and the indoor temperature, the exterior window temperature and the indoor temperature, the roof temperature and the indoor temperature of the energy-saving buildings and the basic building, the indoor environment distribution uniformity of both building types was obtained, and the results are shown in Figure 7, Figure 8 and Figure 9.

As Figure 6 shows, the load of the energy-saving buildings adopting horizontal shading, vertical shading, comprehensive shading, and louver shading was lower than that of the basic building with the specific results shown in

Table 10. Average value of the differences between different building loads.

	QAV(QB–QH) (W)	QAV(QB–QV) (W)	QAV(QB–QC) (W)	QAV(QB–QS) (W)
March	81.36	47.90	56.04	50.34
April	93.47	56.61	60.48	52.94
May	106.88	65.65	69.61	60.23
September	182.99	132.76	140.98	127.69
October	178.63	132.76	142.77	130.82
November	88.67	58.17	63.54	54.86

.

In fact, Table 10 shows the average difference in building load among the different buildings. Here, the load of the basic building was 122.00 W higher than that of the horizontal-shading-based energy-saving buildings, 82.31 W higher than that of the vertical-shading-based energy-saving buildings, 88.91 W higher than that of the comprehensive-shading-based energy-saving buildings, and 79.48 W higher than that of the louver-shading-based energy-saving buildings.

As shown in Figure 7, the difference between the exterior wall temperature and the indoor temperature of the basic building was larger than that of the energy-saving buildings regardless of whether they featured horizontal shading, vertical shading, comprehensive shading, or louver-based shading. The specific comparison results are shown in

Table 11. Range of differences between exterior wall temperature and indoor temperature of different buildings.

	March	April	May	September	October	November
Horizontal shading (°C)	0.02–1.31	0.04–1.45	0.08–1.20	0.02–1.06	0.07–1.25	0.01–1.12
Vertical shading (°C)	0.01–1.30	0.04–1.49	0.07–1.21	0.02–1.05	0.06–1.26	0.01–1.26
Comprehensive shading (°C)	0.02–1.31	0.04–1.49	0.09–1.21	0.02–1.06	0.07–1.25	0.01–1.12
louver–based shading (°C)	0.02–1.31	0.04–1.49	0.09–1.21	0.03–1.07	0.08–1.25	0–1.25
Basic building (°C)	0.08–2.35	0.72–2.76	1.32–2.36	1.21–2.55	0.35–2.44	0–1.25

.

As the table shows, during the transition periods, the external wall temperature was, on average, 0.64 °C higher than the indoor temperature with horizontal shading, 0.65 °C higher with vertical shading, 0.64 °C higher with comprehensive shading, and 0.65 °C higher with louver-based shading. Meanwhile, the external wall temperature was 1.45 °C higher than the average indoor temperature in the basic building.

As Figure 8 shows, on comparing the external window temperature with the indoor temperature of the buildings incorporating the same shading design, the difference was found to be larger in the basic building than in the energy-saving buildings across all shading designs. The specific comparison results are shown in

Table 12. Range of differences between exterior window temperature and indoor temperature of different buildings.

	March	April	May	September	October	November
Horizontal shading (°C)	0–1.44	0.05–1.21	0.02–0.68	0.06–0.82	0.02–1.16	1.16–0.07
Vertical shading (°C)	0.06–2.48	0.04–1.95	0.01–0.85	0.02–1.58	0–2.25	0.01–1.67
Comprehensive shading (°C)	0.03–1.44	0.04–1.22	0–0.67	0–0.83	0.02–1.19	1.19–0.07
Louver–based shading (°C)	0.07–1.19	0.04–1.45	0.03–1.23	0.01–0.84	0.03–1.19	0.07–1.10
Basic building (°C)	0.09–1.59	0.01–2.01	0–1.81	0.04–2.12	0.03–2.34	0.09–1.59

.

As the table shows, during the transition periods, the external window temperature was, on average, 0.56 °C higher than the indoor temperature with horizontal shading, 0.91 °C higher with vertical shading, 0.56 °C higher with comprehensive shading, and 0.60 °C with louver-based shading, while it was 0.99 °C higher in the basic building.

As Figure 9 shows, on comparing the roof temperature and the indoor temperature of the buildings incorporating the same shading design, the difference was found to be larger in the basic building than in the energy-saving buildings across all types of shading design. The specific comparison results are shown in

Table 13. Range of differences between roof temperature and indoor temperature of different buildings.

	March	April	May	September	October	November
Horizontal shading (°C)	0.14–1.72	0.01–1.26	0.06–1.00	0–0.61	0.01–0.89	0–1.00
Vertical shading (°C)	0.14–1.71	0.01–1.26	0.05–0.99	0.01–0.60	0–0.89	0–0.89
Comprehensive shading (°C)	0.15–1.73	0.02–1.26	0.06–1.01	0–0.62	0–0.89	0.01–1.01
Louver-based shading (°C)	0.15–1.74	0.02–1.26	0.06–1.01	0–0.62	0–0.89	0.03–0.89
Basic building (°C)	0.60–3.02	0.12–3.02	0.18–2.32	0.39–2.44	0.66–2.91	0.54–2.71

.

As the table shows, during the transition periods, the roof temperature was, on average, 0.56 °C higher than the indoor temperature with horizontal shading, 0.54 °C higher with vertical shading, 0.56 °C higher with comprehensive shading, and 0.56 °C higher with louver-based shading, while it was, on average, 1.39 °C higher in the basic building.

As shown in Figure 10, on comparing the indoor temperature of the four types of shaded energy-saving buildings with that of the basic building, it was found that it was higher in the former than in the latter. The specific results are shown in

Table 14. Distribution range of indoor temperature in different buildings.

	THI–TBI (°C)	TVI–TBI (°C)	TCI–TBI (°C)	TSI–TBI (°C)
March	0.82–1.49	0.78–1.50	0.83–1.50	0.83–1.50
April	0.39–2.11	0.56–1.46	0.60–1.46	0.60–1.46
May	0.56–1.16	0.55–1.15	0.57–1.17	0.57–1.17
September	0.26–1.07	0.28–1.12	0.28–1.09	0.29–1.10
October	0.30–1.28	0.28–1.28	0.31–1.29	0.31–1.29
November	0.59–1.32	0.57–1.29	0.59–1.33	0.59–1.33

.

As the table shows, the indoor temperature in the energy-saving buildings with horizontal shading was, on average, 2.11 °C higher than in the basic building, while in the energy-saving buildings with vertical shading, comprehensive shading, and louver-based shading, it was, on average, 1.5 °C higher.

8. Conclusions

A large number of single buildings in Wenzhou area were investigated and analyzed in terms of the energy consumption habits of their residents from 2010 to 2020, the current situation and the shortcomings of the building structures, and the building envelopes and the indoor environment. Based on the findings, various energy-saving buildings with different building envelopes (external walls, roofs, external windows, and shading designs) were constructed. Here, there were 12 types of wall material and 50 types of roof-related variables, including plant height, leaf area index, leaf reflectivity, soil roughness, thickness, conductivity, and soil-specific heat. Meanwhile, the external windows were divided into 21 types with different window frames and different glass types, while the shading designs were divided into horizontal shading, vertical shading, comprehensive shading, and louver-based shading. Here, there were 79 specific variables, including transmittance, the extension length of the sun visor, and louver angle.

ENERGYPLUS 9.5 software and the TOPSIS calculation method were used to calculate the respective C_i values of the wall series 1 to series 12, the roof series 13 to series 60, the window series 61 to series 81, and the sunshade series 82 to series 160. The results indicated that the solution that is closest to the ideal could be achieved when the wall material is sintered hollow brick, when the height of the plants on the planting roof is 0.2 m, when the leaf area index is 1, the leaf reflectivity is 0.8, and when the soil has the following parameters: the soil roughness is smooth with a thickness of 0.05 m, a soil conductivity of 0.7, a soil-specific heat of 1500 J/kg·°C, and a soil density of 2000 kg/m³. Meanwhile, the ideal solution could be achieved when the window type is the u = 2.8 PVC plastic–steel type + ordinary hollow type. In terms of the horizontal shading design, the closest to the ideal solution could be achieved when the transmittance is 0.15 and the extension length of the sun visor is 100 mm, while with the vertical shading design, the transmittance should be 0.8 and the extension length of the sun visor 100 mm, and with the louver-based design, the transmittance should be 0.8 and the louver angle 160°.

A comparative analysis was conducted using the RANCOM method, wherein the four variables—façade materials, planted roofs, window materials, and shading forms—were weighted in descending order regarding their influence on building energy consumption, specifically: window materials, façade materials, planted roofs, and shading forms. These results align with those obtained through the TOPSIS method.

The indoor environmental parameters and load values of the four energy-saving buildings were compared with those of the basic building. Here, it was found that the average load of the energy-saving buildings was 79.48 to 122.00 W lower than that of the basic building, the average difference between the exterior wall temperature and the interior temperature of the energy-saving buildings was 0.64 to 1.45 °C lower than that of the basic building, the average difference between the exterior window temperature and the interior temperature of the energy-saving buildings was 0.56 to 0.98 °C lower than that of the basic building, the average difference between the roof temperature and the room temperature of the energy-saving buildings was 0.54 to 0.39 °C lower than that of the basic building, and the average indoor temperature of the energy-saving buildings was 1.50 to 2.11 °C higher than that of the basic building.

The economic efficiency of the energy-saving buildings incorporating different shading designs was also calculated with the four energy-saving buildings and the basic building compared based on the heat calculation formula [41]. During the 183 days of the transitional periods, the total heat reduction was 165.75 to 479.84 kg, 171.9 to 444.80 kg, 181.0 to 446.50 kg, and 182.00 to 447.90 kg, respectively. On comparing the above reduced heat values with those related to standard coal, given that the calorific value per kilogram of standard coal is 29.27 MJ, the four energy-saving buildings could save up to 5.66 to 16.39 kg, 5.87 to 15.20 kg, 6.18 to 15.25 kg, and 6.22 to 15.30 kg, respectively [42]. Meanwhile, based on the estimation that 3.67 kg of CO₂ is emitted per kilogram of standard coal, the CO₂ reduction with the four buildings was 3.16 to 4.47 kg, 1.60 to 4.14 kg, 1.69 to 4.16 kg, and 1.69 to 4.17 kg, respectively [43].

The investigation of energy-saving envelope structure combinations in the Wenzhou area offers insights into energy-saving combinations for envelope structure materials in tropical monsoon climate zones. It lays the groundwork for reducing building energy consumption. However, current discussions, both domestically and internationally, predominantly focus on the energy-saving technology of envelope structures with limited attention paid to the energy-saving impact of other equipment and measures for individual buildings. To enhance the energy-saving efficiency of single buildings in tropical monsoon climate areas, there is a need for further exploration into the energy-saving effects of additional equipment and measures.