Utilizing interpretable stacking ensemble learning and NSGA-Ⅲ for the prediction and optimisation of building photo-thermal environment and energy consumption

作     者：Yeqin Shen Yubing Hu Kai Cheng Hainan Yan Kaixiang Cai Jianye Hua Xuemin Fei Qinyu Wang Yeqin Shen;Yubing Hu;Kai Cheng;Hainan Yan;Kaixiang Cai;Jianye Hua;Xuemin Fei;Qinyu Wang

作者机构：School of ArtsJiangsu UniversityZhenjiang212013China School of Architecture and Urban PlanningNanjing UniversityNanjing210093China School of Computer Science and Communication EngineeringJiangsu UniversityZhenjiang212013China 

出 版 物：《Building Simulation》 (建筑模拟（英文）)

年 卷 期：2024年第17卷第5期

页      面：819-838页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0907[农学-林学] 08[工学] 081304[工学-建筑技术科学] 0813[工学-建筑学] 0814[工学-土木工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0713[理学-生态学] 

基　　金：funded by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23-2117) 

主　　题：building ecological performance ensemble learning multi-objective optimisation sustainable design explainable machine learning 

摘      要：This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of *** integrated model consists of five base models and a meta-model,which significantly improves the prediction ***,the R2 value was improved by 9.19% and the error metrics MAE,MSE,MAPE,and CVRMSE were reduced by 69.47%,79.88%,67.32%,and 57.02%,respectively,compared to the single prediction *** to the research on interpretable machine learning,adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the *** the multi-objective optimisation part,we used the NSGA-Ⅲ algorithm to successfully improve the energy efficiency,daylight utilisation and thermal comfort of the ***,the optimal design solution reduces the energy use intensity by 31.6 kWh/m^(2),improves the useful daylight index by 39%,and modulated the thermal comfort index,resulting in a decrement of 0.69℃ for the summer season and an enhancement of 0.64℃ for the winter season,***,this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency,daylight utilisation and thermal comfort optimisation in an integrated manner,providing an important support for achieving sustainable building design.