Operation strategy for engineered natural ventilation using machine learning under sparse data conditions
Operation strategy for engineered natural ventilation using machine learning under sparse data conditions
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DOI：
10.1002/2475-8876.12255
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发表时间：
2021-12
影响因子：
0.9
通讯作者：
K. Hiyama;Kenichiro Takeuchi;Yuichi Omodaka;T. Srisamranrungruang
中科院分区：
文献类型：
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作者：
K. Hiyama;Kenichiro Takeuchi;Yuichi Omodaka;T. Srisamranrungruang
机器学习(ML)是一种改进建筑操作的有用技术。然而，如果数据只能从目标建筑获得，数据短缺将限制一般ML模型的使用。为了克服这一问题，需要简化目标和有限数量的特征变量。在建筑工程中，建筑物理可以用来促进ML的实施。本文针对工程自然通风的运行进行了基于能量模拟的案例研究。目标问题被简化，以选择全天最好的打开模式，全开或半开。对使用两种或三种特征变量类型的两种模型进行了比较。即使在使用的第一年，ML方法也能有效地预测正确的操作方案。在第二年，三种变量类型的正确率从83%上升到95%，尽管在两种变量类型的情况下没有观察到显著的改善。结果表明，在建筑作业的ML模型实施中，首先创建一个简化的模型，然后在数据采集之后改进模型的策略。
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.