Multi-Objective Optimization-Driven Research on Rural Residential Building Design in Inner Mongolia Region
Abstract
:1. Introduction
2. Literature Review
2.1. Multi-Objective Optimisation of Building Energy Efficiency
2.2. Village Housing Performance Study
3. Methodology
3.1. Overview of the Study
3.2. Multi-Objective Optimisation Model Creation
3.2.1. Residential Wall Database Construction
3.2.2. Multi-Objective Optimisation Theory and Model Creation
3.2.3. Model-Related Parameter Settings
3.3. Parameter Sensitivity Analysis
3.3.1. Optimisation Parameter Correlation Analysis
3.3.2. Sensitivity Analysis of Building Energy Consumption
3.3.3. Sensitivity Analysis of Thermal Comfort
3.3.4. Sensitivity Analysis of Indoor Lighting
3.4. Case Study
3.4.1. Case Overview
3.4.2. Case Building Related Parameters
3.5. Performance Analysis of Typical Rural Buildings
3.5.1. Analysis of Building Energy Consumption
3.5.2. Building Thermal Comfort Analysis
3.5.3. Building Interior Lighting Analysis
4. Results and Discussion
4.1. Analysis of Pareto Solution Set Distribution
4.2. Solution Set Options
4.3. Design Parameter Optimisation Solution Set
4.4. Comparison of Cases and Exploration of Universality
5. Limitations and Future Research Directions
5.1. Limitations
5.2. Future Research Directions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name of Material | Thermal Conductivity W/(m·K) | Vapour Permeability Coefficient G/(m·h.Pa) | Densities Kg/m³ | Heat Storage Capacity W/(m2·K) | |
---|---|---|---|---|---|
Insulation material | Extruded polystyrene sheet | 0.030 | — | 35 | 0.34 |
Moulded Polystyrene Sheet | 0.039 | — | 30 | 0.36 | |
Moulded Polystyrene Sheet | 0.041 | — | 20 | 0.36 | |
Rock wool | 0.040 | 4.880 | 180 | 0.70 | |
Aerogel | 0.016 | — | 3.55 | — | |
Concrete | Shale ceramsite concrete | 0.500 | 0.435 | 1100 | 6.70 |
Haydite concrete | 0.84 | 0.315 | 1600 | 10.36 | |
Volcanic ash, sand, cement concrete | 0.57 | 0.395 | 1700 | 6.30 | |
Fly ash ceramsite concrete | 0.44 | 1.350 | 1100 | 6.30 | |
Finishing materials | Marble | 2.91 | 0.113 | 2800 | 23.27 |
Granite | 3.49 | 0.113 | 2800 | 25.49 | |
Woodwork | 0.35 | 3.000 | 700 | 6.93 | |
Mortar | Vitrified microsphere insulation slurry | 0.080 | — | 350 | — |
Rubber powder polystyrene particle thermal insulation mortar | 0.070 | — | 300 | — |
Field Name | Data Type | Null Value | Clarification | |
---|---|---|---|---|
No. | id | Int | N | |
Wall Name | w-name | varchar | N | Record the name of the wall |
Wall Number | w-num | varchar | N | Numbering the walls |
Image Name | img-name | varchar | N | Recording of wall construction images |
Image Data | img-path | mediumblob | N | Data path for recording images |
Image Number | img-num | varchar | N | Numbering the images |
Material Number | m-num | varchar | N | For wall construction materials |
Material Content | material | tinytext | Y | Description of wall construction materials, remarks text |
Comparison Dimension | Grasshopper + Wallacei | Other Multi-Objective Optimisation Tools |
---|---|---|
User Interface and Interactivity | Based on a visual programming environment (Grasshopper), it supports node-based parametric operations and is suitable for users with a non-programming background. | Mostly traditional GUIs or code-driven interfaces (e.g., jMetal requires programming), with a steeper learning curve. |
Optimisation algorithm flexibility | Based on NSGA-II algorithm, supports customised fitness functions; suitable for small to medium sized problems. | Provide a broader library of algorithms (e.g., MOEA/D, SPEA2) and support large-scale multidisciplinary optimisation (e.g., ModeFRONTIER). |
Visualisation and post-processing | Built-in interactive 3D visualisation and dynamic Pareto Frontier analysis supports simultaneous exploration of design space and target space. | Reliance on external tools (e.g., MATLAB R2023a, ParaView 5.12) for advanced visualisation and process fragmentation. |
Domain Applicability | For building and engineering design optimisation, it supports the coupled analysis of morphology generation and environmental performance simulation. | More versatile and suitable for traditional engineering fields such as mechanical and aerospace, but needs to be customised to suit construction needs. |
Time | Bedroom | Living Room | Kitchen | Toilet | Ancillary Rooms |
---|---|---|---|---|---|
00:00 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
01:00 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
02:00 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
03:00 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
…… | …… | …… | …… | …… | …… |
21:00 | 0.5 | 0.5 | 0.0 | 0.5 | 0.1 |
22:00 | 1.0 | 0.0 | 0.0 | 0.1 | 0.1 |
23:00 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
24:00 | 1.0 | 0.0 | 0.0 | 0.0 | 0.0 |
Optimisation Platforms | Rhino-Grasshopper and Wallacei | ||
---|---|---|---|
Optimisation goals | Building energy consumption | Indoor thermal comfort | Indoor Lighting |
Optimisation of target indicators | EUI | PPD | UDI |
No. | Parametric | Starting Value | Range of Values |
---|---|---|---|
1 | Shading overhang dimensions | 0.11 m | 0.3–1.5 m |
2 | Shading element light transmittance | 0.62 | 0–1 |
3 | Reflectivity of internal walls | 0.10 | 0–1 |
Ceiling reflectivity | 0.5 | 0–1 | |
Ground reflectivity | 0.8 | 0–1 | |
4 | Internal wall thickness | 0.17 m | 0.1–0.4 m |
5 | Floor thermal resistance | 3.0 m2·K/W | 0.7–6 m2·K/W |
6 | Window thermal transmittance coefficient | 0.9 (W/m2·K) | 0–2.5 (W/m2·K) |
7 | Glazing solar heat gain coefficient | 1.0 | 0–1 |
8 | Wall database | - | - |
Parameter Name | Set Value |
---|---|
Population Size | 20 |
Generation count | 50 |
Crossover probability | 0.8 |
Mutation probability | 1/r |
Crossover distribution index | 20 |
Mutation distribution index | 20 |
Random seed | 1 |
Parametric | p-Value | Parametric | p-Value |
---|---|---|---|
Shading overhang dimensions | 1.74 × 10−14 | Internal wall thickness | 5.56 × 10−10 |
Shading element light transmittance | 2.28 × 10−24 | Floor thermal resistance | 5.32 × 10−19 |
Reflectivity of internal walls | 2.08 × 10−15 | Window thermal transmittance coefficient | 1.23 × 10−14 |
Ceiling reflectivity | 1.92 × 10−13 | Glazing solar heat gain coefficient | 2.53 × 10−14 |
Ground reflectivity | 6.1 × 10−19 | - | - |
Parametric | Permutation Importance Method | Gradient Boosting Trees |
---|---|---|
Shading overhang dimensions | 0.313 (± 0.164) | 0.2569 |
Shading element light transmittance | 0.056 (± 0.024) | 0.0782 |
Reflectivity of internal walls | 0.126 (± 0.047) | 0.1018 |
Ceiling reflectivity | −0.026 (± 0.020) | 0.0182 |
Ground reflectivity | 0.026 (± 0.013) | 0.0243 |
Internal wall thickness | 0.280 (± 0.045) | 0.2249 |
Floor thermal resistance | 0.339 (± 0.101) | 0.3253 |
Window thermal transmittance coefficient | 0.025 (± 0.015) | 0.0284 |
Glazing solar heat gain coefficient | 0.013 (± 0.009) | 0.0112 |
Parametric | Permutation Importance Method | Gradient Boosting Trees |
---|---|---|
Shading overhang dimensions | 0.192 (±0.087) | 0.1908 |
Shading element light transmittance | 0.120 (±0.044) | 0.0120 |
Reflectivity of internal walls | 0.041 (±0.039) | 0.0773 |
Ceiling reflectivity | 0.003 (±0.023) | 0.0255 |
Ground reflectivity | 0.078 (±0.018) | 0.0792 |
Internal wall thickness | 0.135 (±0.039) | 0.0788 |
Floor thermal resistance | 0.216 (±0.111) | 0.2154 |
Window thermal transmittance coefficient | 0.383 (±0.023) | 0.4071 |
Glazing solar heat gain coefficient | 0.030 (±0.020) | 0.0334 |
Parametric | Permutation Importance Method | Gradient Boosting Trees |
---|---|---|
Shading overhang dimensions | 0.125 (±0.0023) | 0.1073 |
Shading element light transmittance | 0.235 (±0.018) | 0.1453 |
Reflectivity of internal walls | 1.120 (±0.011) | 0.8708 |
Ceiling reflectivity | 0.033 (±0.001) | 0.0013 |
Ground reflectivity | 0.0002 (±0) | 0.00006 |
Internal wall thickness | 0.045 (±0.003) | 0.0026 |
Floor thermal resistance | 0.001 (±0.001) | 0.0010 |
Window thermal transmittance coefficient | 0.0005 (±0) | 0.0002 |
Glazing solar heat gain coefficient | 0.0008 (±0) | 0.0005 |
Classification | Composition | Thermal Conductivity Thermal Resistance (m2·K/W) | Thermal Inertness Index | Heat Transfer Coefficient [W/(m2·K)] |
---|---|---|---|---|
Roof | Cement mortar 2 mm + asphalt felt 6 mm + cement mortar 40 mm + extruded polystyrene board 75 mm + clay ceramsite concrete 200 mm | 2.727 | 4.068 | 0.347 |
Floor | Cement mortar 25 mm + reinforced concrete 80 mm + cement mortar 20 mm | 0.098 | 1.262 | 3.054 |
External wall | Cement mortar 10 mm + extruded polystyrene board 60 mm + shale multi-hollow brick 240 mm + lime cement mortar 20 mm | 2.332 | 0.402 | 4.245 |
Classification | Name | Heat Transfer Coefficient [W/ (m2·K)] | Visible Light Transmittance |
---|---|---|---|
Window | Aluminium alloy-70 series flat window (5 + 12A + 5Low-E) | 1.9 | 0.8 |
Gate | Single-layer solid wooden outer door | 1.972 | - |
Type of Energy Consumption | Heating Load | Cooling Load | Lighting Load | Equipment Load |
---|---|---|---|---|
EUI (kWh/m2) | 186.622 | 7.086 | 14.672 | 18.07 |
Percentage | 82.41% | 3.13% | 6.48% | 7.98% |
Months | January | February | March | April | May | June |
Percentage | 20.70% | 15.59% | 11.45% | 4.72% | 2.44% | 2.28% |
Months | July | August | September | October | November | December |
Percentage | 2.39% | 1.20% | 1.59% | 6.10% | 12.85% | 18.69% |
Indoor Space Temperature | Outer Surface Temperature of Building | PPD |
---|---|---|
Objectives | Starting Value | Single Best Value | Optimisation Rate |
---|---|---|---|
EUI (kWh/m2) | 226.449 | 129.406 | 42.85 |
PPD (%) | 27.828 | 21.178 | 23.90 |
UDI (%) | 48.770 | 63.747 | 30.71 |
Performance Ranking Optimum | EUI (kWh/m2) | PPD (%) | UDI (%) | Diamond Radar Chart |
---|---|---|---|---|
Optimal building energy consumption | 129.406 | 39.659 | 57.884 | |
Optimal indoor thermal comfort | 206.831 | 25.152 | 63.747 | |
Optimal Useful Daylight Illuminance | 177.913 | 21.178 | 48.504 | |
Programmes with the highest average ranking | 175.348 | 22.467 | 61.178 |
Objectives | Starting Value | Integrated Optimal Value | Optimisation Rate |
---|---|---|---|
EUI (kWh/m2) | 226.449 | 175.348 | 22.56 |
PPD (%) | 27.828 | 22.467 | 19.26 |
UDI (%) | 48.770 | 61.178 | 25.44 |
Objectives | Starting Value | Integrated Optimal Value | Optimisation Rate |
---|---|---|---|
Heating | 186.622 | 138.95 | 25.54% |
Air Conditioning | 7.086 | 3.657 | 48.39% |
Lighting | 14.672 | 14.669 | 0 |
Equipment | 18.07 | 18.07 | 0 |
No. | Parametric | Optimal Value |
---|---|---|
1 | Shading overhang dimensions | 0.10 |
2 | Shading element light transmittance | 0.90 |
3 | Reflectivity of internal walls | 0.10 |
Ceiling reflectivity | 0.20 | |
Ground reflectivity | 0.20 | |
4 | Internal wall thickness | 0.19 |
5 | Floor thermal resistance | 0.7 |
6 | Window thermal transmittance coefficient | 1.3 |
7 | Glazing solar heat gain coefficient | 0.6 |
8 | Wall database | 254 |
No. | Composition | Heat Transfer Coefficient [W/ (m2·K)] |
---|---|---|
No. 254 | 1.10 mm exterior finish 2.90 mm extruded polystyrene sheet 3.240 mm shale ceramic concrete 4.20 mm internal plaster on external wall | 0.326 |
Comparison Dimension | This Study in Inner Mongolia | Zalantun Case [34] | Northwest Territories Cases [2] | TianJin Case [35] | Xuzhou Case [36] |
---|---|---|---|---|---|
Climate zone | Severely Cold Zone | Severely Cold Zone | Cold Zone | Cold Zone | Summer Hot and Winter Cold Zone |
Optimisation goals | Energy consumption, Indoor thermal comfort, Indoor Lighting | Energy consumption, Costs | Energy consumption, Indoor thermal comfort, Costs | Energy consumption, Indoor thermal comfort | Indoor thermal comfort, Carbon emission, Costs |
Core algorithm | NSGA-II (Wallacei) | NSGA-II (MOBO) | NSGA-II (MOBO) | SPEA-2, HypE | Krill Herd Algorithm |
Core variables | Enclosure, Form | Enclosure insulation | Orientation, External windows, Insulation | Architectural form | Window–wall ratio, Heat transfer coefficient |
Energy saving rate increase | 22.56% | 34.1~72.0% | 54% | 22.8% | Reduced by 55.84 kWh/m2 |
Range of cost increments | Small increase | 92.3–645.5 CNY/m2 | 182.4 CNY/m2 | - | - |
Thermal comfort improvement | PPD decreased by 19.26% | - | APDD increased by 57.6% | Reduction in the number of hours of thermal discomfort by 8.8% | APD decreased by 25% |
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Share and Cite
Zou, D.; Sun, C.; Gao, D. Multi-Objective Optimization-Driven Research on Rural Residential Building Design in Inner Mongolia Region. Energies 2025, 18, 1867. https://doi.org/10.3390/en18071867
Zou D, Sun C, Gao D. Multi-Objective Optimization-Driven Research on Rural Residential Building Design in Inner Mongolia Region. Energies. 2025; 18(7):1867. https://doi.org/10.3390/en18071867
Chicago/Turabian StyleZou, Dezhi, Cheng Sun, and Denghui Gao. 2025. "Multi-Objective Optimization-Driven Research on Rural Residential Building Design in Inner Mongolia Region" Energies 18, no. 7: 1867. https://doi.org/10.3390/en18071867
APA StyleZou, D., Sun, C., & Gao, D. (2025). Multi-Objective Optimization-Driven Research on Rural Residential Building Design in Inner Mongolia Region. Energies, 18(7), 1867. https://doi.org/10.3390/en18071867