Predicting the opening state of a group of windows in an open-plan office by using machine learning models

Abstract

Window operation is among one of the most influencing factors on the indoor air quality (IAQ). The opening state of the windows can modify the air exchange rate and as such the pollutant transfer between indoor and outdoor environments. In this paper, we focus on the modeling of the windows opening state in a real open-plan office with five windows. For this purpose, three machine learning-based models were implemented: (i) Decision Tree, (ii) k-Nearest Neighbors and (iii) Kernel Approximation. IAQ, climatic parameters and the opening state of the windows have been monitored during an entire period of 18 months. The information about: (i) the environmental factors from the previous 24th hour and (ii) the current time (month, day of the week, hour of the day) was used to predict the current state of the windows. The predictor importance estimation and the calculated autocorrelation functions showed that the three most relevant factors were: the previous 24th hour of the windows status, the current time and the previous 24th hour of the prevailing mean outdoor air temperature. The three models perform well with the testing sets according to the different evaluation indicators. The developed methods can be helpful for understanding occupant behavior and also for controlling indoor air pollutants levels in buildings, either as a standalone model or a part of a real-time IAQ monitoring system.