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Thermal comfort prediction by applying supervised machine learning in green sidewalks of Tehran

SMART AND SUSTAINABLE BUILT ENVIRONMENT 2020年第4期9卷 361-374页

作者： Eslamirad, Nasim Malekpour Kolbadinejad, Soheil Mahdavinejad, Mohammadjavad Mehranrad, Mohammad Tarbiat Modares Univ Dept Architectural Technol Tehran Iran Univ Marlik Dept Architecture Nowshahr Iran Islamic Azad Univ Dept Architecture Cent Tehran Branch Tehran Iran

Purpose This research aims to introduce a new methodology for integration between urban design strategies and supervised machine learning (SML) method - by applying both energy engineering modeling (evaluating phase) for the existing green sidewalks and statistical energy modeling (predicting phase) for the new ones - to offer algorithms that help to catch the optimum morphology of green sidewalks, in case of high quality of the outdoor thermal comfort and less errors in results. Design/methodology/approach The tools of the study are the way of processing by SML, predicting the future based on the past. Machine learning is benefited from Python advantages. The structure of the study consisted of two main parts, as the majority of the similar studies follow: engineering energy modeling and statistical energy modeling. According to the concept of the study, at first, from 2268 models, some are randomly selected, simulated and sensitively analyzed by ENVI-met. Furthermore, the Envi-met output as the quantity of thermal comfort - predicted mean vote (PMV) and weather items are inputs of Python. Then, the formed data set is processed by SML, to reach the final reliable predicted output. Findings The process of SML leads the study to find thermal comfort of current models and other similar sidewalks. The results are evaluated by both PMV mathematical model and SML error evaluation functions. The results confirm that the average of the occurred error is about 1%. Then the method of study is reliable to apply in the variety of similar fields. Finding of this study can be helpful in perspective of the sustainable architecture strategies in the buildings and urban scales, to determine, monitor and control energy-based behaviors (thermal comfort, heating, cooling, lighting and ventilation) in operational phase of the systems (existed elements in buildings, and constructions) and the planning and designing phase of the future built cases - all over their life spans. Research li

关键词： Urban thermal comfort Green Sidewalks hyper parameters learning algorithm Supervised machine learning

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