Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods
Designing energy-efficient buildings is essential for reducing global energy consumption and carbon emissions. However, traditional physics-based simulation models require substantial computational resources, detailed input data, and domain expertise. To address these limitations, this study investi...
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MDPI AG
2025-07-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/13/2361 |
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| author | Navid Shirzadi Dominic Lau Meli Stylianou |
| author_facet | Navid Shirzadi Dominic Lau Meli Stylianou |
| author_sort | Navid Shirzadi |
| collection | DOAJ |
| description | Designing energy-efficient buildings is essential for reducing global energy consumption and carbon emissions. However, traditional physics-based simulation models require substantial computational resources, detailed input data, and domain expertise. To address these limitations, this study investigates the use of three machine learning-based surrogate models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP)—trained on a synthetic dataset of 2000 EnergyPlus-simulated building design scenarios to predict both energy use intensity (EUI) and cost estimates for midrise apartment buildings in the Toronto area. All three models exhibit strong predictive performance, with R<sup>2</sup> values exceeding 0.9 for both EUI and cost. XGBoost achieves the best performance in cost prediction on the testing dataset with a root mean squared error (RMSE) of 5.13 CAD/m<sup>2</sup>, while MLP outperforms others in EUI prediction with a testing RMSE of 0.002 GJ/m<sup>2</sup>. In terms of computational efficiency, the surrogate models significantly outperform a physics-based simulation model, with MLP running approximately 340 times faster and XGBoost and RF achieving over 200 times speedup. This study also examines the effect of training dataset size on model performance, identifying a point of diminishing returns where further increases in data size yield minimal accuracy gains but substantially higher training times. To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. A parametric design configuration analysis further evaluates the models’ sensitivity to changes in building envelope features. Overall, the findings demonstrate that machine learning-based surrogate models can serve as fast, accurate, and interpretable alternatives to traditional simulation methods, supporting efficient decision-making during early-stage building design. |
| format | Article |
| id | doaj-art-968fa04ae06440069bef2e3624a8709b |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-968fa04ae06440069bef2e3624a8709b2025-08-20T03:17:52ZengMDPI AGBuildings2075-53092025-07-011513236110.3390/buildings15132361Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based MethodsNavid Shirzadi0Dominic Lau1Meli Stylianou2CanmetENERGY-Ottawa, Natural Resources Canada, Ottawa, ON K1A 1M1, CanadaCanmetENERGY-Ottawa, Natural Resources Canada, Ottawa, ON K1A 1M1, CanadaCanmetENERGY-Ottawa, Natural Resources Canada, Ottawa, ON K1A 1M1, CanadaDesigning energy-efficient buildings is essential for reducing global energy consumption and carbon emissions. However, traditional physics-based simulation models require substantial computational resources, detailed input data, and domain expertise. To address these limitations, this study investigates the use of three machine learning-based surrogate models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP)—trained on a synthetic dataset of 2000 EnergyPlus-simulated building design scenarios to predict both energy use intensity (EUI) and cost estimates for midrise apartment buildings in the Toronto area. All three models exhibit strong predictive performance, with R<sup>2</sup> values exceeding 0.9 for both EUI and cost. XGBoost achieves the best performance in cost prediction on the testing dataset with a root mean squared error (RMSE) of 5.13 CAD/m<sup>2</sup>, while MLP outperforms others in EUI prediction with a testing RMSE of 0.002 GJ/m<sup>2</sup>. In terms of computational efficiency, the surrogate models significantly outperform a physics-based simulation model, with MLP running approximately 340 times faster and XGBoost and RF achieving over 200 times speedup. This study also examines the effect of training dataset size on model performance, identifying a point of diminishing returns where further increases in data size yield minimal accuracy gains but substantially higher training times. To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. A parametric design configuration analysis further evaluates the models’ sensitivity to changes in building envelope features. Overall, the findings demonstrate that machine learning-based surrogate models can serve as fast, accurate, and interpretable alternatives to traditional simulation methods, supporting efficient decision-making during early-stage building design.https://www.mdpi.com/2075-5309/15/13/2361surrogate modelingmachine learningbuilding energy modelingenergy use intensitycost estimationearly-stage building design |
| spellingShingle | Navid Shirzadi Dominic Lau Meli Stylianou Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods Buildings surrogate modeling machine learning building energy modeling energy use intensity cost estimation early-stage building design |
| title | Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods |
| title_full | Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods |
| title_fullStr | Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods |
| title_full_unstemmed | Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods |
| title_short | Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods |
| title_sort | surrogate modeling for building design energy and cost prediction compared to simulation based methods |
| topic | surrogate modeling machine learning building energy modeling energy use intensity cost estimation early-stage building design |
| url | https://www.mdpi.com/2075-5309/15/13/2361 |
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