基于机器学习的办公建筑热舒适度预测研究  

Research on Thermal Comfort Prediction of Office Buildings Based on Machine Learning

作　　者：方正燃 舒欣 FANG Zheng-ran;SHU Xin(College of Architecture,Nanjing Tech University)

机构地区：[1]南京工业大学建筑学院

出　　处：《智能建筑与智慧城市》2025年第4期171-173,共3页Intelligent Building & Smart City

摘　　要：在工业化和环境保护并重的背景下,中国提出“双碳”政策,推动了建筑行业的减碳行动。建筑应通过体量、开窗、朝向等优化自然采光、通风、采暖,以提高室内热舒适度。文章以热舒适度理论为基础,利用机器学习技术和Python编程,通过PMV值预测办公建筑在不同条件下的热舒适度,开发基于机器学习的室内热舒适度预测程序。Against the backdrop of equal emphasis on industrialization and environmental protection,China introduced the"dual carbon"policy to promote carbon reduction actions in the construction industry.Buildings should optimize elements like volume,window placement,and orientation to enhance natural lighting,ventilation,and heating,thereby improving indoor thermal comfort.Based on thermal comfort theory,this paper uses machine learning technology and Python programming to predict the thermal comfort of office buildings under various conditions through PMV value,and develops indoor thermal comfort prediction program based on machine learning.

关 键 词：办公建筑 室内热舒适 机器学习 

分 类 号：TU111[建筑科学—建筑理论]