{"title":"基于机器学习的代用模型,用于在设计阶段快速评估新建筑对城市局部小气候的影响","authors":"","doi":"10.1016/j.buildenv.2024.112142","DOIUrl":null,"url":null,"abstract":"<div><div>The rapid urbanization introduces changes in the local urban microclimate. Many efforts have been paid on the impact assessment of building design on local microclimate. However, there is still a lack of efficient and accurate prediction method for assessing the impacts on local microclimate when making the design of individual buildings. Two complementary machine learning-based surrogate models are proposed, including an SVR-based local air temperature model and a LightGBM-based local wind velocity model. They are identified by evaluating and comparing eight alternative machine learning models, four for each model development. 200 sets of CFD simulation data corresponding to different building designs are used for the model training and validation. The results show that the developed surrogate models can dramatically reduce computation time (from over 5 h to less than a second for a single prediction) while keeping the same order of accuracy of CFD simulations for local microclimate prediction of individual buildings. It therefore facilitates the fast, comprehensive and accurate prediction of the impacts on the local microclimate at the early design stage of new construction and renovation of individual buildings, for designers to deliver preferred local microclimate and/or avoid unacceptable microclimate changes.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based surrogate models for fast impact assessment of a new building on urban local microclimate at design stage\",\"authors\":\"\",\"doi\":\"10.1016/j.buildenv.2024.112142\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The rapid urbanization introduces changes in the local urban microclimate. Many efforts have been paid on the impact assessment of building design on local microclimate. However, there is still a lack of efficient and accurate prediction method for assessing the impacts on local microclimate when making the design of individual buildings. Two complementary machine learning-based surrogate models are proposed, including an SVR-based local air temperature model and a LightGBM-based local wind velocity model. They are identified by evaluating and comparing eight alternative machine learning models, four for each model development. 200 sets of CFD simulation data corresponding to different building designs are used for the model training and validation. The results show that the developed surrogate models can dramatically reduce computation time (from over 5 h to less than a second for a single prediction) while keeping the same order of accuracy of CFD simulations for local microclimate prediction of individual buildings. It therefore facilitates the fast, comprehensive and accurate prediction of the impacts on the local microclimate at the early design stage of new construction and renovation of individual buildings, for designers to deliver preferred local microclimate and/or avoid unacceptable microclimate changes.</div></div>\",\"PeriodicalId\":9273,\"journal\":{\"name\":\"Building and Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Building and Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0360132324009843\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Building and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360132324009843","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
Machine learning-based surrogate models for fast impact assessment of a new building on urban local microclimate at design stage
The rapid urbanization introduces changes in the local urban microclimate. Many efforts have been paid on the impact assessment of building design on local microclimate. However, there is still a lack of efficient and accurate prediction method for assessing the impacts on local microclimate when making the design of individual buildings. Two complementary machine learning-based surrogate models are proposed, including an SVR-based local air temperature model and a LightGBM-based local wind velocity model. They are identified by evaluating and comparing eight alternative machine learning models, four for each model development. 200 sets of CFD simulation data corresponding to different building designs are used for the model training and validation. The results show that the developed surrogate models can dramatically reduce computation time (from over 5 h to less than a second for a single prediction) while keeping the same order of accuracy of CFD simulations for local microclimate prediction of individual buildings. It therefore facilitates the fast, comprehensive and accurate prediction of the impacts on the local microclimate at the early design stage of new construction and renovation of individual buildings, for designers to deliver preferred local microclimate and/or avoid unacceptable microclimate changes.
期刊介绍:
Building and Environment, an international journal, is dedicated to publishing original research papers, comprehensive review articles, editorials, and short communications in the fields of building science, urban physics, and human interaction with the indoor and outdoor built environment. The journal emphasizes innovative technologies and knowledge verified through measurement and analysis. It covers environmental performance across various spatial scales, from cities and communities to buildings and systems, fostering collaborative, multi-disciplinary research with broader significance.