Huang Yiyang, He Zhizhuo, Qin Yuchen, Lu Yichen, Chen Kaida
School of Civil Engineering and Architecture, Wuyi University, Nanping, 354300, China.
School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan, 430074, China.
Sci Rep. 2025 Mar 10;15(1):8193. doi: 10.1038/s41598-025-85267-w.
At present, the evaluation of the comprehensive performance of urban office buildings remains an area of significant discussion. This research aims to optimize the building performance of office buildings in the hot summer and warm winter (HSWW) region, focusing on three key aspects: energy use intensity (EUI), useful daylight illuminance (UDI), and percentage of thermal comfort (PTC). The study employs the Hyperparameter Optimization (Hyperopt)-Categorical Boosting (CatBoost)-Strength Pareto Evolutionary Algorithm 2 (SPEA2) multi-objective optimization method, generating 3,000 datasets via Latin Hypercube Sampling (LHS). Building performance parameters are simulated using the Ladybug and Honeybee models, and energy consumption and comfort levels are predicted using the CatBoost model. Subsequently, Hyperopt is used to optimize hyperparameters, and the SPEA2 algorithm is applied to identify Pareto optimal solutions. The results indicate that Hyperopt-CatBoost demonstrates excellent predictive performance, with R² values of 0.996, 0.954, and 0.985 for energy consumption, lighting, and thermal comfort, respectively. By using the SPEA2 multi-objective optimization (MOO) algorithm to optimize design parameters, energy consumption is reduced by 29.61%, lighting efficiency improves by 59.61%, and comfort increases by 37.69% compared to the original design. This study provides a systematic optimization plan and data support for energy-saving design, improving comfort, and enhancing lighting efficiency for office building renovation in urban villages.
目前，城市办公建筑综合性能的评估仍是一个备受讨论的领域。本研究旨在优化夏热冬暖地区办公建筑的性能，重点关注三个关键方面：能源使用强度（EUI）、有效日光照度（UDI）和热舒适度百分比（PTC）。该研究采用超参数优化（Hyperopt）-分类提升（CatBoost）-强度帕累托进化算法2（SPEA2）多目标优化方法，通过拉丁超立方抽样（LHS）生成3000个数据集。使用Ladybug和Honeybee模型模拟建筑性能参数，并使用CatBoost模型预测能耗和舒适度水平。随后，使用Hyperopt优化超参数，并应用SPEA2算法识别帕累托最优解。结果表明，Hyperopt-CatBoost具有出色的预测性能，能耗、照明和热舒适度的R²值分别为0.996、0.954和0.985。与原始设计相比，通过使用SPEA2多目标优化（MOO）算法优化设计参数，能耗降低了29.61%，照明效率提高了59.61%，舒适度提高了37.69%。本研究为城中村办公建筑改造的节能设计、提高舒适度和提升照明效率提供了系统的优化方案和数据支持。
