Operation strategy for engineered natural ventilation using machine learning under sparse data conditions

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.

Deep-Learning-Based Natural Ventilation Rate Prediction with Auxiliary Data in Mismeasurement Sensing Environments

“…However, it is widely recognized that the numerical analysis approach is challenging for analyzing the dependent relationships among various parameters. To overcome this limitation, a method utilizing machine learning to design a predictive model considering the complex relationship between these various parameters has been introduced [14][15][16][17]. In particular, ref.…”

“…Recently, research has been conducted to predict the amount of natural ventilation based on machine learning for data convergence analysis using various IoT sensing data that affect indoor and outdoor airflow [14]. In the studies by [14][15][16][17], the amount of natural ventilation was predicted using machine learning, and the predictability of reducing energy demand and the efficient ventilation of buildings was investigated.…”