Multi-Objective Optimization of Envelope Design of Rural Tourism Buildings in Southeastern Coastal Areas of China Based on NSGA-II Algorithm and Entropy-Based TOPSIS Method
Abstract
:1. Introduction
1.1. Background
1.2. Indoor Thermal Environment and Energy Consumption of RTBs
Building Types | Requirements | Standards | |
---|---|---|---|
Roof: U ≤ 0.8~1.0 W/(m2·K) External wall: U ≤ 1.5~1.8 W/(m2·K) Door: U ≤ 3.0 W/(m2·K) Window: U ≤ 3.2~4.7 W/(m2·K) | GB/T 50824-2013 [21] Voluntary | ||
RTBs | Ordinary RRBs | ||
Roof: U ≤ 0.4 W/(m2·K) External wall: U ≤ 0.6~1.2 W/(m2·K) Window: U ≤ 2.0~2.8 W/(m2·K); SHGC ≤ 0.5 (winter)/0.25~0.4 (summer) Door: U ≤ 2.0 W/(m2·K) Floor: U ≤ 1.8 W/(m2·K) | GB 55015-2021 [22] Mandatory | ||
URBs | |||
Roof: U ≤ 0.4 W/(m2·K) External wall: U ≤ 0.6~0.8 W/(m2·K) Window: U ≤ 3.0~1.8 W/(m2·K); SHGC ≤ 0.45~0.20 Floor: U ≤ 0.7 W/(m2·K) | GB 55015-2021 [22] Mandatory | ||
Hotels and Restaurants (building area ≥ 300 m2) |
1.3. Studies on Multi-Objective Optimization of Building Performance
1.4. Aims of the Research
2. Methodology
2.1. Field Survey of RTBs
- (1)
- The RTBs were farmers’ self-built and self-occupied houses.
- (2)
- The RT business operators were the farmers themselves.
- (3)
- The RT business had been carried out for more than 5 years and had a high occupancy rate.
- (4)
- The RTBs had similar housing equipment systems.
- (5)
- The business operators were willing to allow energy monitors and other measuring equipment to be installed in guestrooms.
- (6)
- The business operators were willing to provide guest information such as gender, age, and check-in and check-out times.
2.1.1. The Building Characteristics of RTBs
2.1.2. The Characteristics of Energy Consumption in RTBs
2.1.3. People’s Activities and Equipment Usage in RTBs
2.2. Model Establishment
2.2.1. Benchmark Models
2.2.2. Model Verification and Validation
2.3. Analysis Methods
2.3.1. Sensitivity Analysis
- (1)
- SA simulations are carried out, and the matrixes of the SRC values are established:
- (2)
- The contribution rates of the design variables are calculated for each objective, and the contribution rate matrixes () are constructed:
- (3)
- The average contribution rate (ACR) is calculated to obtain the average ACR matrix:
2.3.2. Multi-Objective Optimization
2.3.3. TOPSIS Decision-Making Method
- (1)
- The judgment matrix is constructed. Considering the differences in the attributes in units and orders of magnitude, the matrix is normalized using the MMN method. The structure of the normalized matrix can be expressed as follows:
- (2)
- The attribute weight () is calculated. The process of determining the weight for the entropy-based TOPSIS method involves establishing a normalized judgment matrix for each evaluation index and calculating the weight of each index based on the entropy value (), which can be expressed using the following equation:Then, the attribute weight () can be calculated using the following equation:
- (3)
- The positive ideal solution () and negative ideal solution () are determined using the weighted normalized decision matrix.
- (4)
- The Euclidean distances from each alternative scheme to the positive ideal solution ( and the negative ideal solution () are calculated.
- (5)
- The relative closeness () of each alternative scheme is calculated, and the preference order is ranked:
2.3.4. Evaluation Metrics
3. Results and Discussions
3.1. The Results of the Sensitivity Analysis
3.2. The Results of Multi-Objective Optimization
3.3. A Comprehensive Evaluation of TOPSIS
4. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Content | Type 1 | Type 2 |
---|---|---|
Location | Latitude: N 31.17°; Longitude: E 121.43° | Latitude: N 31.17°; Longitude: E 121.43° |
Orientation | North and south | North and south |
Structure | Stone structure | Brick–concrete structure; thermal bridge: 20% |
WWR | South: 25%; North: 20% | South: 30%; North: 25% |
External walls | 400 mm limestone wall; U = 2.3 W/(m2·K) | 240 mm fired perforated brick wall; U = 1.8 W/(m2·K) |
External windows | Ordinary aluminum-alloy single-layer glass window; U = 6.0 W/(m2 K); SHGC = 0.75 | Ordinary aluminum-alloy single-layer glass window; U = 6.0 W/(m2 ·K); SHGC = 0.75 |
Roof | Pitched roof (unoccupied): U = 6K W/(m2·K) | Flat roof: U = 3.8 W/(m2·K) |
Airtightness | 1.0 ach/h | 1.0 ach/h |
Variables | Units | Base Value | Range | Step | Distribution | Abbr. |
---|---|---|---|---|---|---|
Wall U value | W/(m2·K) | 2.2 | 0.4~2.6 | 0.2 | uniform | Wall-U |
Windows U value | W/(m2·K) | 6.4 | 1.5~3.5 | 0.2 | uniform | Win-U |
Solar heat gain coefficient | - | 0.8 | 0.2~0.8 | 0.2 | uniform | SHGC |
Roof U value | W/(m2·K) | 3.5/6.0 | 0.5~6.0 | 0.2 | uniform | Roof-U |
External door U value | W/(m2·K) | 2.8 | 2.0~3.0 | 0.2 | uniform | Door-U |
Window-to-wall ratio (south) | % | 30 | 20~80 | 5 | uniform | WWR-S |
Window-to-wall ratio (north) | % | 30 | 15~80 | 5 | uniform | WWR-N |
Shading (south) | m | - | 0.3~1.2 | 0.3 | uniform | Shading-S |
Shading (east/west) | m | - | 0.3~1.2 | 0.3 | uniform | Shading-EW |
Infiltration | ach/h | 1.0 | 0.3~1.5 | 0.2 | uniform | Infiltration |
Lighting power density | W/(m2) | 7 | 4~8 | 1 | uniform | Lighting |
Equipment power density | W/(m2) | 15 | 3~15 | 2 | uniform | Equipment |
Occupancy number of people (guest room) | P/room | 3 | 1~4 | 1 | uniform | ONP |
Heating set-point temperature | °C | 18 | 16~21 | 1 | uniform | Heating ST |
Cooling set-point temperature | °C | 26 | 22~28 | 1 | uniform | Cooling ST |
Zone | People (P/room) | Metabolic Rate (W/P) | Temperature (°C) | Lighting (W/m2) | Equipment (W/m2) | |
---|---|---|---|---|---|---|
Summer | Winter | |||||
Guestroom | 3 | 100 | 26 | 18 | 7 | 15 |
Bedroom | 2 | 100 | 26 | 18 | 7 | 15 |
Living room | 10 | 110 | 26 | 18 | 7 | 15 |
Kitchen | 3 | 160 | / | / | 7 | 70 |
WC | 1 | 120 | / | / | 7 | 15 |
Envelope | Material | Range | Cost (CNY/m2) |
---|---|---|---|
Wall | Inorganic light-weight aggregate insulating mortar | U = 1.0~1.8 | 25~45 |
Roof | EPS board | U = 0.4~2.0 | 10~120 |
Shading | Concrete shading board | L = 0~1.2 m | 60 |
Window | Ordinary aluminum window (6 mm) | U = 6.0; SHGC=0.75 | 250 |
Ordinary aluminum window (heat-absorbing glass (6 mm)) | U = 6.0; SHGC = 0.45 | 280 | |
Ordinary aluminum window (6 mm, low-e) | U = 5.3; SHGC = 0.50 | 350 | |
Ordinary aluminum window (6 + 12 A + 6 mm) | U = 3.5; SHGC = 0.70 | 450 | |
Ordinary aluminum window (6 + 12 A + 6 mm, low-e) | U = 3.0; SHGC = 0.45 | 550 | |
Thermal-break aluminum window (6 mm) | U = 5.5; SHGC = 0.75 | 350 | |
Thermal-break aluminum window (heat-absorbing glass (6 mm)) | U = 5.0; SHGC = 0.45 | 380 | |
Thermal-break aluminum window (6 mm, low-e) | U = 4.5; SHGC = 0.50 | 480 | |
Thermal-break aluminum window (6 + 12 A + 6 mm) | U = 3.0; SHGC = 0.70 | 680 | |
Thermal-break aluminum window (6 + 12 A + 6 mm, low-e) | U = 2.5; SHGC = 0.45 | 780 |
Type | Schemes | CEC (kWh/m2) | ADH (h) | GC (CNY/m2) | NSE (kWh/m2) | Passive Design Parameters |
---|---|---|---|---|---|---|
Type 1 | Best CEC/Best NSE/Best GC | 13.58 | 2890.5 | 225.7 | 47.56 | Wall-U: 1.0W/(m2·K); WWR-S: 20%; WWR-N: 15%; ordinary aluminum window (heat-absorbing glass (6 mm)): U6.0 W/(m2·K), SGHC0.45 |
Best ADH | 15.82 | 2816.8 | 418.3 | 49.52 | Wall-U: 1.0W/(m2·K); WWR-S: 45%; WWR-N: 55%; thermal-break aluminum window (6 + 12 A + 6 mm): U3.0 W/(m2·K), SGHC0.70 | |
TOPSIS | 14.59 | 2826.8 | 306.8 | 48.35 | Wall-U: 1.0W/(m2·K); WWR-S: 20%; WWR-N: 15%; thermal-break aluminum window (6 + 12 A + 6 mm): U3.0 W/(m2·K), SGHC0.70 | |
Type 2 | Best CEC/Best NSE/Best GC | 10.36 | 2860.4 | 215.0 | 42.82 | Roof-U: 0.4 W/(m2·K); Wall-U: 1.0W/(m2·K); WWR-S: 25%; WWR-N: 20%; ordinary aluminum window (heat-absorbing glass (6 mm)): U6.0 W/(m2·K), SGHC0.45; Shading-S: 1.2 m; Shading-EW: 1.2 m |
Best ADH | 13.23 | 2758.4 | 372.9 | 45.58 | Roof-U: 0.4 W/(m2·K); Wall-U: 1.0W/(m2·K); WWR-S: 65%; WWR-N: 45%; thermal-break aluminum window (6 + 12 A + 6 mm): U3.0 W/(m2·K), SGHC0.70; Shading-S: 0 m; Shading-EW: 0 m | |
TOPSIS | 11.00 | 2802.3 | 294.7 | 43.39 | Roof-U: 0.4 W/(m2·K); Wall-U: 1.0W/(m2·K); WWR-S: 25%; WWR-N: 20%; thermal-break aluminum window (6 + 12 A + 6 mm, low-e): U2.5 W/(m2·K), SGHC0.45; Shading-S: 0.3 m; Shading-EW: 0 m |
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Wang, M.; Chen, C.; Fan, B.; Yin, Z.; Li, W.; Wang, H.; Chi, F. Multi-Objective Optimization of Envelope Design of Rural Tourism Buildings in Southeastern Coastal Areas of China Based on NSGA-II Algorithm and Entropy-Based TOPSIS Method. Sustainability 2023, 15, 7238. https://doi.org/10.3390/su15097238
Wang M, Chen C, Fan B, Yin Z, Li W, Wang H, Chi F. Multi-Objective Optimization of Envelope Design of Rural Tourism Buildings in Southeastern Coastal Areas of China Based on NSGA-II Algorithm and Entropy-Based TOPSIS Method. Sustainability. 2023; 15(9):7238. https://doi.org/10.3390/su15097238
Chicago/Turabian StyleWang, Meiyan, Chen Chen, Bingxin Fan, Zilu Yin, Wenxuan Li, Huifang Wang, and Fang’ai Chi. 2023. "Multi-Objective Optimization of Envelope Design of Rural Tourism Buildings in Southeastern Coastal Areas of China Based on NSGA-II Algorithm and Entropy-Based TOPSIS Method" Sustainability 15, no. 9: 7238. https://doi.org/10.3390/su15097238