摘要
现有碳排放计算方法存在数据量大、计算繁琐和仅适用于事中、事后控制等问题,不利于设计人员在设计阶段进行碳减排工作。为此,研究将机器学习引入建筑物碳排放量计算领域,帮助设计人员在早期设计阶段获得建筑物物化阶段的碳排放信息,提供碳减排参考。首先,收集并建立建筑物物化阶段碳排放数据库;其次,基于5个建筑物特征,建立4种不同类型的机器学习模型,并根据评价指标对模型性能进行评价;最后,利用沙普利加和解释(Shapley Additive exPlanations, SHAP)和部分依赖图(Partial Dependence Plot, PDP)验证最优模型应用的合理性,并深入挖掘各特征与碳排放之间的复杂关系,为建筑物碳减排提供新的信息。结果显示:各机器学习模型可以很好地预测建筑物物化阶段碳排放过程,其中建立的极度随机树(Extremely Randomized Trees, ET)模型对碳排放的预测表现最优;机器学习模型各特征对预测结果的影响与现有研究相似,表明了机器学习模型预测结果的可靠性与合理性;机器学习模型可以深入挖掘各特征与碳排放之间的复杂关系,为建筑物碳减排提供新的指导。
Existing carbon emission calculation methods suffer from drawbacks such as excessive data requirements,complicated calculations,and applicability limited to after-action control,thereby not promoting carbon emission reduction work in the early design stage by designers.To mitigate these problems,machine learning is introduced in this paper for carbon emission calculations in buildings to help designers obtain carbon emission information at an early stage of the building design and provide reduction references.This paper has the following procedures:(1)collection and establishment of a database of carbon emissions in the materialization stage of buildings,and checking of the database quality through Mahalanobis distance;(2)establishment of four machine learning models,namely Extremely Randomized Trees(ET),eXtreme Gradient boosting,(XGboost),Multilayer Perceptron(MLP)and Support Vector Regression(SVR),based on five building characteristics,and evaluation of the model performance using three evaluation indices-R 2,Mean Absolute Error(E MAE),and Root Mean Square Error(E RMSE).(3)using SHapley Additive ExPlanations(SHAP)and Partial Dependence Plot(PDP)to prove the appropriateness of applying the ET models,thereby exploring the complex relationships between each feature and carbon emission.The outcome provides new information for carbon emission reduction in buildings.The findings indicate that:(1)Each machine learning model can efficiently predict carbon emissions in buildings physical phases.Among these,the ET model established in this paper exhibits the best performance with an R 2 of 0.88,an E RMSE of 0.317 kt CO 2,and an E MAE of 0.217 kt CO 2.(2)Interpretable analyses of the ET model by SHAP and PDP show that the effects of the features of the ET model on the prediction results are similar to those of existing studies,which verifies the reliability and reasonableness of the prediction results of the machine learning model.(3)The impact of each feature on the carbon emission of the materialization stage of buildin
作者
王志强
任金哥
韩硕
李文超
WANG Zhiqiang;REN Jinge;HAN Shuo;LI Wenchao(School of Management Engineering,Qingdao University of Technology,Qingdao 266520,Shandong,China;Smart City Construction Management Research Center(New Think Tank),Qingdao 266520,Shandong,China;Architectural Design and Research Institute,Qingdao University of Technology,Qingdao 266520,Shandong,China)
出处
《安全与环境学报》
CAS
CSCD
北大核心
2024年第6期2454-2466,共13页
Journal of Safety and Environment
基金
国家自然科学基金项目(71471094)
青岛理工大学人文社会科学研究基金项目(Crw2023-006)。
关键词
环境工程学
建筑物物化阶段
碳排放
机器学习
可解释性分析
environmental engineering
materialization stage of building
carbon emission
machine learning
interpretability analysis
