Optimizing the sustainable performance of public buildings: A hybrid machine learning algorithm

Addressing energy challenges in public buildings is essential for achieving sustainable development. This paper presents a hybrid intelligence prediction and optimization framework based on Building Information Modeling-Design Builder (BIM-DB) simulations. The framework incorporates a hybrid algorit...

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Vydané v:Energy (Oxford) Ročník 320; s. 135283
Hlavní autori: Xu, Wen, Wu, Xianguo, Xiong, Shishu, Li, Tiejun, Liu, Yang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.04.2025
Predmet:
air
algorithms
Bayesian theory
BIM-DB
BO-CatBoost-NSGA-III
carbon
data collection
energy
hybrids
Knowledge‒data dual‒driven
Multiobjective optimization
prediction
SHAP
Sustainability performance of public buildings
sustainable development
Knowledge‒data dual‒driven
BO-CatBoost-NSGA-III
SHAP
Sustainability performance of public buildings
BIM-DB
Multiobjective optimization
ISSN:0360-5442
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Shrnutí:Addressing energy challenges in public buildings is essential for achieving sustainable development. This paper presents a hybrid intelligence prediction and optimization framework based on Building Information Modeling-Design Builder (BIM-DB) simulations. The framework incorporates a hybrid algorithm that combines Bayesian optimization (BO), categorical boosting (CatBoost), and the nondominated sorting genetic algorithm-III (NSGA-III). A dual-driven knowledge‒data framework is established to predict and optimize sustainability objectives, such as those related to building energy consumption (BEC), carbon emissions (CE), the thermal comfort value (PMV), and the glare index (GI). A public building in Wuhan city was selected to assess the practical feasibility of the proposed method. The results indicate the following: (1) Building parameters and thresholds were established through knowledge-driven methods, and a sample dataset was generated via BIM-DB simulations and Latin hypercube sampling (LHS), with the BO-CatBoost hybrid algorithm achieving fitness values above 0.9 for all the targets. (2) The BO-CatBoost-NSGA-III algorithm optimized sustainability performance, yielding reductions of 32.20 % in BEC, 48.77 % in CE, 60.69 % in PMV, and 15.45 % in GI. (3) A comparison with other methods demonstrated the high accuracy and reliability of the BO-CatBoost approach, whereas SHAP analysis identified key factors such as air conditioning settings and building envelope and orientation. Integrating knowledge-driven and data-driven approaches can significantly enhance the sustainability of public buildings, offering a novel research framework for future studies. •A hybrid intelligent optimization algorithm based on BO-CatBoost-NSGA-III is proposed.•BIM-DB and LHS methods have played an important role in collecting energy data.•Optimizing building design parameters reduces energy consumption in public buildings.•The interpretability of hybrid intelligent algorithms can be achieved through SHAP.
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ISSN:0360-5442
DOI:10.1016/j.energy.2025.135283