Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold Regions of China
Abstract
:1. Introduction
2. Methods
2.1. Overview Workflow
- (1)
- Simulation site and analysis objects settings: A benchmark courtyard model was selected by simulating and ranking PET and MRT values across various courtyard types using Honeybee 0.0.66 software.
- (2)
- Multi-objective genetic optimization (MOGO) for courtyards. The benchmark courtyard model underwent refinement through MOGO using the Wallacei_X plugin, generating a dataset for subsequent machine learning.
- (3)
- Wind and thermal environment analysis for street networks. Performance simulations of the wind environment within street networks were conducted, and their correlation with spatial elements was analyzed. Additionally, thermal environment simulations at different analysis radii were performed to examine the relationship between comfort and spatial elements, leading to the establishment of stepwise regression predictive equations.
- (4)
- Predictive model construction based on machine learning: The courtyard dataset was processed in Scikit-learn 1.3 and trained using the XGBoost algorithm to create a predictive model for courtyard performance.
- (5)
- Program evaluation: Various design plans were proposed based on different target objectives. The data parameters of each solution were input into the algorithm model for performance evaluation, allowing for the selection of the most optimal design.
2.2. Simulation Site and Analysis Objects Settings
2.2.1. Site Selection for This Study
- (1)
- Mountainous Location: Village A is situated in a mountainous area. According to ArcGIS 10.8 statistical analysis of village distribution in Shandong Province, most traditional villages are in mountainous regions. Using a cluster statistical analysis method, this study overlayed the distribution of 519 villages in Shandong with a 30-m resolution digital elevation model (DEM) of the province, resulting in a topographic map of village distribution (Figure 2). The statistical results reveal that approximately 57% of the villages are situated at elevations exceeding 100 m (Table 1), primarily concentrated in the central mountainous region of Shandong.
- (2)
- Distance from Urban Areas: Village A is far from urban centers. To ensure the independence of the study subject and minimize the influence of urban heat island effects, the selected site is over 40 km away from several city centers (Figure 3). The mountainous terrain further reduces the impact of urban heat island effects.
2.2.2. Microclimate Measurements and Validation
- (1)
- Honeybee’s simulation of indoor comfort is based on outdoor meteorological elements and does not offer a separate input interface for indoor data;
- (2)
- This study focused on performance comparisons between multiple schemes, emphasizing the relative improvement or degradation in performance rather than the building performance of a single scheme;
- (3)
- Since various factors influence buildings during operation, the load settings for building energy units can vary significantly, with no unified standard. Therefore, this study only explored the energy consumption of buildings under ideal conditions, examining the differences in energy consumption between various schemes and the correlation with building parameters.
2.2.3. Baseline Courtyards Model and Environment Setup
2.3. Multi-Objective Genetic Optimization (MOGO) Based on Courtyards
2.3.1. Design Parameters and Performance Objectives Selection
- (1)
- Outdoor Comfort: This criterion is based on Honeybee’s outdoor comfort autonomy modules, using PET (Physiological Equivalent Temperature) as the evaluation index. The goal is to maximize the proportion of time within the 5–31 °C threshold, a range deemed acceptable for cold regions according to related studies [27,30]. The analysis period aligns with the benchmark model from the previous stage, focusing on typical weeks in summer and winter (20–26 July and 23–29 December, respectively). The results are denoted as “OTCA_C” for winter and “OTCA_H” for summer.
- (2)
- Indoor Comfort: Similar to the outdoor comfort model, the indoor comfort autonomy modules assess the proportion of comfortable time under natural ventilation, utilizing the adaptive comfort model proposed by De Dear and Brager [31]. The analysis concentrates on the typical summer week (20–26 July). In northern China, where heating is provided from November to March, indoor comfort generally remains within comfort ranges under steady-state conditions, rendering further analysis of a typical winter week unnecessary. The results are represented by “ITCA_H”.
- (3)
- Indoor Illuminance: This criterion evaluates indoor lighting conditions over a year using spatial Daylight Autonomy (sDA) as the index. sDA measures the proportion of time in a year that achieves an illuminance level of 300 lux by the “Standard for Lighting Design of Buildings” [32]. The results are indicated by “sDA”.
- (4)
- Building Energy Consumption: Energy Use Intensity (EUI) was chosen as the evaluation standard, referring to the energy consumption per unit building area (kWh/m²) over one year. Since energy consumption values tend to be larger than other indicators, EUI was normalized to a range of 0–1 for easier comparison.
2.3.2. Simulation Generation Setup
2.4. Simulation and Correlation Analysis of Wind and Thermal Environment Performance Based on Street Networks
2.5. Machine Learning Settings
2.5.1. Data Preprocessing
2.5.2. Data Splitting
2.5.3. Algorithm Selection, Model Setting, and Validation of Model Training Accuracy
2.6. Solution Validation
3. Results
3.1. Baseline Model Selection
3.2. Simulation of Courtyard Performance
3.3. Simulation and Prediction Equation of Street Space Performance
3.3.1. Analysis of Street Thermal Environment Performance
3.3.2. Analysis of Street Wind Environment Performance
3.4. Machine Learning Validation
3.4.1. Analysis of the Influence of Courtyard Design Parameters
3.4.2. Verification Results of Machine Learning Accuracy
3.5. Application and Performance Analysis Statistics
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Elevation (m) | <50 | 50–100 | 100–200 | 200–500 | >500 |
---|---|---|---|---|---|
Number of Villages | 159 | 64 | 134 | 140 | 22 |
Index | Energy Model Parameters |
---|---|
Wall | 5 mm Cement mortar + 370 mm Fired claybrick U-value = 1.72 W·m−2·K−1 |
Floor | 10 mm Ceramic tile + 100 mm Concrete + 1500 mm Plain soil compaction U-value = 0.47 W·m−2·K−1 |
Roof | Color steel plate+ 10 mm Asphalt felt +10 mm Grass clay + 200 mm Cement mortar U-value = 1.94 W·m−2·K−1 |
Glazing | Wooden Glass U-value = 5.03 W·m−2·K−1 SHGC = 0.6 |
Shading | Not applied |
Equipment loads per area | 3.7 W·m−2 |
Infiltration rate per area | 0.4 cfm/sf facade @ 75 Pa |
Lighting density per area | 11.8 W·m−2 |
Num. of people per area | 0.03 people·m−2 |
Schedules | Default Honeybee residential schedules /The Schedule for controlling tree porosity |
HVAC | Ideal mechanical system |
Classification | No. | Orientation | No. of 2-Storey Buildings | Category Code ** | Total Categories | ||
---|---|---|---|---|---|---|---|
Traditional Courtyards (105) | 4-sided | 13 | North–South | 0 | 5c/5h | 17 | |
1 | 2c/2h | ||||||
2 | 1c/1h | ||||||
East–West | 1 | 3c/3h | |||||
3 | 4c/4h | ||||||
3-sided | 45 | North–South | 0 | 7c/7h | |||
1 | 9c/9h | ||||||
2 | 10c/10h | ||||||
East–West | 0 | 6c/6h | |||||
1 | 8c/8h | ||||||
2-sided | 33 | North–South | Central * | 0 | 13c/13h | ||
1 | 12c/12h | ||||||
East | 1 | 11c/11h | |||||
East–West | Central | 1 | 15c/15h | ||||
East | 1 | 14c/14h | |||||
1-sided | 14 | North–South | 0 | 17c/17h | |||
East–West | 1 | 16c/16h |
Design Variables | Unit | Scope |
---|---|---|
Courtyard width | m | [8, 13] |
Standard floor height | m | [2.6, 4.1] |
Orientation | ° | [−45, 45] |
Secondary-house Floor Control | - | [−1, 1] |
Window-to-wall ratio | - | [0, 0.35] |
Performance Objectives | Analysis Period | The Acceptable Range | Scope | |
---|---|---|---|---|
Outdoor Comfort | OTCA_C | Typical Week in Winter | >5 °C | [0, 1] |
OTCA_H | Typical Week in Summer | <32 °C | [0, 1] | |
Indoor Comfort | ITCA_H | Typical Week in Summer | <32 °C | [0, 1] |
sDA | 1 year | >300 l× | [0, 1] | |
Building energy consumption | EUI | 1 year | - | [0, 1] |
Population | |
---|---|
Generation size | 50 |
Generation count | 60 |
Algorithm parameters | |
Crossover probability | 0.9 |
Mutation probability | 1/n |
Crossover distribution index | 20 |
Mutation distribution index | 20 |
Random seed | 1 |
Outdoor_C | Outdoor_H | Indoor_H | Indoor_sDA | EUI | |
---|---|---|---|---|---|
Cluster 1/Individual. 1 | 0.052194 | 0.353481 | 0.892496 | 0.893248 | 0.372975 |
Cluster 2/Individual. 6 | 0.060127 | 0.368541 | 0.793157 | 0.972145 | 0.265634 |
Cluster 3/Individual. 3 | 0.057044 | 0.361921 | 0.739375 | 0.792245 | 0.393248 |
Cluster 4/Individual. 5 | 0.069542 | 0.349952 | 0.897124 | 0.521406 | 0.391205 |
Cluster 5/Individual. 11 | 0.068231 | 0.365781 | 0.926355 | 0.575702 | 0.376893 |
Cluster 6/Individual. 8 | 0.067198 | 0.354203 | 0.885853 | 0.529257 | 0.389754 |
Generations | sDA Value | Performance Label | |
---|---|---|---|
Pareto Front Solutions | 40–59 | >0.7 | A1 |
20–39 | >0.7 | B2 | |
0–19 | >0.7 | C3 | |
0–59 | <0.7 | D4 | |
Non-Front Solutions | 0–59 | >0.7 | E5 |
0–59 | <0.7 | F6 |
Index/Performance Rating | Outdoor Comfort | Indoor Comfort | Building Energy Consumption /EUI | Prediction Accuracy | ||
---|---|---|---|---|---|---|
Outdoor_C Simulation | Outdoor_H Simulation | Indoor_H Simulation | Indoor_sDA Simulation | |||
1/A | 0.051136 | 0.358759 | 0.892034 | 0.89998 | 0.372426 | 89% |
Optimization percentage | ||||||
2/B | 0.069129 | 0.365641 | 0.780046 | 0.79998 | 0.317511 | 82% |
Optimization percentage | ||||||
3/C | 0.060938 | 0.362153 | 0.791221 | 0.98 | 0.262619 | 81% |
Optimization percentage | - | - | - | - | ||
4/D | 0.065476 | 0.35846 | 0.91218 | 0.53334 | 0.385004 | 75% |
Optimization percentage | ||||||
5/E | 0.057044 | 0.361921 | 0.739375 | 0.79998 | 0.390826 | 82% |
Optimization percentage | - | |||||
6/F | 0.056551 | 0.351798 | 0.717885 | 0.78877 | 0.415123 | 88% |
Optimization percentage |
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Xu, Z.; Li, X.; Sun, B.; Wen, Y.; Tang, P. Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold Regions of China. Buildings 2024, 14, 2796. https://doi.org/10.3390/buildings14092796
Xu Z, Li X, Sun B, Wen Y, Tang P. Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold Regions of China. Buildings. 2024; 14(9):2796. https://doi.org/10.3390/buildings14092796
Chicago/Turabian StyleXu, Zhixin, Xiaoming Li, Bo Sun, Yueming Wen, and Peipei Tang. 2024. "Evaluation and Optimization of Traditional Mountain Village Spatial Environment Performance Using Genetic and XGBoost Algorithms in the Early Design Stage—A Case Study in the Cold Regions of China" Buildings 14, no. 9: 2796. https://doi.org/10.3390/buildings14092796