文章基本信息

标题：Operation strategy for engineered natural ventilation using machine learning under sparse data conditions
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作者：Kyosuke Hiyama ; Kenichiro Takeuchi ; Yuichi Omodaka 等
期刊名称：Japan Architectural Review
电子版ISSN：2475-8876
出版年度：2022
卷号：5
期号：1
页码：119-126
DOI：10.1002/2475-8876.12255
语种：English
摘要：Abstract Machine learning (ML) is a useful technique for improving building operations. However, if data can only be obtained from a target building, the data shortage will limit the use of general ML models. To overcome this issue, simplified targets and limited numbers of feature variables are required. In building engineering, building physics can be used to promote ML implementations. In this paper, a case study targeting the operation of engineered natural ventilation is performed based on energy simulations. The target issue is simplified to select the preferable opening pattern, full or half open, throughout the day. Two models using two or three feature variable types are compared. The ML method can effectively predict the correct operation scheme, even in the first year of use. The correct answer rate in the case of three variable types increases from 83% to 95% in the second year, although no significant improvement is observed in the case with two variable types. The results imply the strategy of creating a simplified model first and improving the model following data acquisition works for ML model implementation in building operations.