Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou
Abstract
:1. Introduction
1.1. Background of the Study
1.2. Related Work
1.3. Aims and Originality
- Develop a form parametric model of typical high-rise office buildings in China’s HSWW zone, simulate their performance across diverse form parameter combinations, and train high-fidelity ML surrogate models for energy use intensity (EUI) and useful daylight illuminance (UDI).
- Integrate the surrogate models with GA to establish a computationally efficient multi-objective optimization workflow.
- Provide designers and policymakers with Pareto-optimal solutions and optimal architectural form parameter ranges for balancing energy efficiency and daylight levels in HSWW high-rise office buildings.
2. Methodology
2.1. Development of Parametric Model
- In the planar aspect, the orientation parameter O, representing the rotation angle of the parametric model and simulating building orientation, changes in 15-degree steps and has its value range limited to half a full circle (180 degrees) due to the symmetrical plan. The plan width parameter W and aspect ratio parameter R define the planar size and shape, while the spatial depth parameter D determines the dimensions of office areas and core zones.
- In the vertical aspect, three parameters—floor height parameter FH, windowsill height parameter WSH, and ceiling height parameter CH—are sufficient to construct any common high-rise office facade. Notably, unlike most existing studies that rely on the commonly used window-to-wall ratio (WWR) parameter to control window/curtain wall size, this study utilizes WSH and CH to precisely define the size and vertical position of windows/curtain walls on the building facade. This allows for a more accurate assessment of how window size and placement influence the building’s performance.
- In the shading aspect, three parameters—horizontal sunshade size HSS, vertical sunshade size VSS, and vertical sunshade distance VSD—can model most common building shading configurations. In the parametric model, horizontal sunshades are fixed at the upper edge of windows/curtain walls, while vertical shading panels are evenly distributed along each facade at intervals defined by VSD. Both horizontal and vertical sunshades are oriented at a 90-degree angle to the facade.
2.2. Specification of Material and Thermophysical Parameters
2.3. Setup of Building Operation Schedule
2.4. Selection of Climate Dataset
- CSWD (Chinese Standard Weather Data): A 2005 historical dataset provided by the China Meteorological Administration.
- TMYx (Typical Meteorological Year): A dynamically updated dataset from the U.S. NOAA, incorporating 2007–2021 monthly averages to reflect contemporary climate trends.
2.5. Creation of Building Performance Simulation Datasets
2.6. Machine Learning Algorithm
- Multi-Layer Perceptron (MLP): a prevalent and simple artificial neural network (ANN) architecture comprising input, hidden, and output layers was selected for this study due to its proven capacity to capture complex nonlinear relationships in building performance datasets. The input layer receives the parameters of architectural form, while the output layer generates predicted performance metrics. By incorporating nonlinear activation functions (e.g., ReLU, Sigmoid), MLPs can capture complex input-output relationships, making them well-suited for mapping static or low-dimensional time-series data like building design parameters to performance outcomes.
- Support Vector Regression (SVR): originating from support vector machine (SVM) theory [86], SVR is a regression model that maps low-dimensional data to a high-dimensional feature space using kernel functions (e.g., RBF). By constructing an optimal regression hyperplane, SVR effectively captures latent relationships between input and output variables, making it well-suited for predicting building performance metrics from design parameters. Unlike other continuous variable prediction methods, SVR exhibits robust generalization when applied to unseen data [87], maintaining superior predictive performance even with limited training data—a critical advantage for building optimization workflows constrained by computational resources.
- Random Forest (RF): an ensemble learning method that constructs multiple decision trees for classification and regression tasks, enhancing prediction accuracy and robustness through aggregating tree outputs [54]. This algorithm reduces model variance and mitigates overfitting risks via bootstrap sampling and random feature selection. Due to its insensitivity to noise and missing values, it maintains stable performance even with limited training data, which is a critical advantage over most other ML models. Additionally, tree-based models are favored for their interpretability, enabling transparent analysis of feature contributions to predictions [17].
- XGBoost: a powerful ensemble learning algorithm based on the Gradient Boosting Decision Tree (GBDT) [88]. It enhances prediction accuracy by combining multiple decision trees. Distinguishing itself from GBDT, XGBoost attains superior computational accuracy. It leverages the second-order Taylor expansion formula and incorporates a regularization term into the objective function, effectively mitigating overfitting risks. Currently, it has demonstrated advantages such as fast computation speed, high prediction accuracy, and strong robustness in regression problems and has become a very popular algorithm.
- CatBoost: an open-source GBDT framework developed by Yandex in 2017 [89] specifically designed for handling categorical features in classification, regression, and ranking tasks. Unlike traditional ML algorithms, CatBoost automates categorical feature processing through advanced techniques such as target encoding and combinatorial optimization, eliminating the need for manual pre-processing. This native capability makes CatBoost particularly suitable for unstructured datasets and high-cardinality categorical scenarios. Furthermore, it has demonstrated effectiveness in predicting energy consumption across diverse domains [90], where it often outperforms XGBoost in both prediction accuracy and computational efficiency.
2.7. Multi-Objective Optimization with Machine Learning
3. Results and Discussion
3.1. Analysis of the Building Performance Datasets
3.2. Training and Evaluation of Machine Learning Models
3.3. Interpretability Analysis of Machine Learning Model Based on SHAP
3.4. Performance and Analysis of Optimization
4. Conclusions
- Through comparative analysis of multiple ML algorithms, ensemble ML algorithms are found to effectively capture the complex nonlinear relationships between building form parameters and performance metrics. Among them, the CatBoost algorithm demonstrates the best predictive performance for this study’s target (R2 = 0.94, CVRMSE = 1.59%).
- SHAP analysis shows that horizontal sunshade size (HSS), spatial depth (D), floor height (FH), windowsill height (WSH), vertical sunshade size (VSS), and vertical shading distance (VSD) strongly influence the predictions of the machine learning model. Additionally, by increasing horizontal sunshade sizes, decreasing vertical shading distance, and adjusting building orientation to a slight southeast direction, these form parameters become the most effective for performance optimization, achieving reduced EUI while improving UDI. In general, SHAP analysis indicates that shading parameters have the greatest effect on performance results, followed by vertical parameters, with planar parameters exerting the smallest influence.
- The Pareto-optimal morphological parameters generated by the surrogate model show good agreement with their corresponding actual simulation results, with 87.7% (57 out of 65) of the results having an error rate below 5% and an average error rate of 0.34% for EUI and −1.4% for UDI. This demonstrates the effectiveness of the integrated optimization approach using machine learning and genetic algorithms.
- Compared to the baseline model, a Pareto-optimal solution achieves a 3.31% reduction in EUI and a 5.12% increase in UDI.
- Based on the Pareto-optimal solutions, the following design strategies for form parameters are proposed to fully enhance the energy-saving potential of high-rise office buildings in China’s HSWW zone: (1) adopting a building orientation ranging from due south to 30 degrees east of south; (2) using a rectangular floor plan measuring approximately 40 m in width and 58 m in length (an aspect ratio of 1.45, total area of about 2300 m2, and office area depth of 12 m); (3) implementing a facade design with a floor height of 4.0–4.2 m, larger possible windowsill and ceiling height, and a window-to-wall ratio of 0.37–0.45; and (4) employing horizontal and vertical sunshades longer than 1.3 m as well as high-density vertical sunshades.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
HSWW | Hot-summer and warm winter |
BPS | Building performance simulation |
BPO | Building performance optimization |
EUI | Energy use intensity |
UDI | Useful daylight illuminance |
ML | Machine learning |
NSGA-II | Non-dominated sorting genetic algorithm |
SHAP | SHapley Additive exPlanation |
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Classification | Form Parameters | Range | Units | Steps | Properties | Baseline |
---|---|---|---|---|---|---|
Planar parameters | Orientation (O) 1 | [0, 180] | degree | 15 | Independent | 90 |
Plan width (W) | [30, 50] | m | 0.1 | Independent | 45 | |
Aspect ratio (R) | [1, 1.5] | - | 0.05 | Independent | 1 | |
Spatial depth (D) | [8, 14] | m | - | Independent | 12.5 | |
Plan length (L) | [30, 75] | m | - | Covariates 2 | 45 | |
Plan area (A) | [900, 3750] | m | - | Covariates 2 | 2025 | |
Vertical parameters | Floor height (FH) | [3.9, 4.5] | m | 0.1 | Independent | 4.2 |
Ceiling height (CH) | [1, 1.5] | m | 0.1 | Independent | 1.2 | |
Windowsill height (WSH) | [0.1, 1.2] | m | 0.1 | Independent | 0.1 | |
Window height (WH) | [1.2, 3.4] | m | - | Covariates 2 | 2.9 | |
Window-wall ratio (WWR) | [30, 75] | % | - | Covariates 2 | ~69 | |
Building storey (BS) 3 | 15 | - | - | Fixed | 15 | |
Shading parameters | Horizontal sunshade size (HSS) | [0.3, 1.5] | m | 0.1 | Independent | 0.9 |
Vertical sunshade size (VSS) | [0.3, 1.5] | m | 0.1 | Independent | 0.9 | |
Vertical sunshade distance (VSD) | [3, 9] | m | 0.1 | Independent | 3 |
Envelope | Thermal Conductivity [W/(m2·K)] | Solar Heat Gain Coefficient (SHGC) | Visible Transmittance | |
---|---|---|---|---|
Curtain 1 | Transmitting | 1.5 | - | - |
Opaque | 2.4 | 0.2 | 0.6 | |
Internal wall | 2.1 | - | - | |
Floor | 1.1 | - | - | |
Ground | 1.5 | - | - | |
Roof | 0.4 | - | - |
Classification | Components | Values |
---|---|---|
People | Occupant heat power | 120 W/people |
Occupant density | 10 m2/people | |
Occupant period | From 7 AM to 9 PM on weekdays | |
Lighting | Illuminance | 300 lx |
Lighting power | 8 W/m2 | |
Operating period | From 7 AM to 9 PM on weekdays | |
HVAC | Outdoor airflow rate | 30 m3/(h × people) |
Cooling temperature setpoint | 26 °C | |
Heating temperature setpoint | Off 1 | |
Coefficient of Performance (COP) | 4.0 | |
Operating period | From 7 AM to 9 PM on weekdays |
Model Name | Results | ||
---|---|---|---|
R2 | RMSE | CVRMSE (%) | |
MLP | 0.8728 | 0.2486 | 5.96% |
SVR | 0.4476 | 0.5182 | 37.89% |
RF | 0.8224 | 0.2938 | 15.1% |
XGBoost | 0.8672 | 0.2541 | 8.89% |
CatBoost | 0.9406 | 0.1930 | 1.57% |
Performance | Minimum | Maximum | Median | Mean | Baseline |
---|---|---|---|---|---|
EUI (kWh/m2) | 31.95 | 33.21 | 32.36 | 32.45 | 33.46 |
UDI (%) | 62.41 | 83.18 | 71.25 | 72.36 | 73.64 |
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Xie, X.; Ni, Y.; Zhang, T. Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou. Sustainability 2025, 17, 4090. https://doi.org/10.3390/su17094090
Xie X, Ni Y, Zhang T. Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou. Sustainability. 2025; 17(9):4090. https://doi.org/10.3390/su17094090
Chicago/Turabian StyleXie, Xie, Yang Ni, and Tianzi Zhang. 2025. "Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou" Sustainability 17, no. 9: 4090. https://doi.org/10.3390/su17094090
APA StyleXie, X., Ni, Y., & Zhang, T. (2025). Machine-Learning-Enhanced Building Performance-Guided Form Optimization of High-Rise Office Buildings in China’s Hot Summer and Warm Winter Zone—A Case Study of Guangzhou. Sustainability, 17(9), 4090. https://doi.org/10.3390/su17094090