A probabilistic multi-variable hybrid approach to window operations and indoor comfort in residential dorms

Abstract

Occupant window interaction is a critical component in optimizing energy consumption and indoor environmental quality (IEQ). Understanding the influence of environmental and behavioral factors on window state decisions remains a significant challenge in building management systems (BMS). We present a hybrid probabilistic model to assess thermal comfort and predict the probability of the occupant opening or closing the window. The data was acquired from an open-source platform that provided yearly university dormitory window interactions. Bayesian networks (BNs) and logistic regression (LR) models were applied to predict the window-opening behavior of the occupants. An average accuracy of 92% for Bayesian and 94% for LR were obtained. The results were further enhanced by combining these models through weighted methods, with weights extrapolated through generative recursive iterations generating an average accuracy of 95% and Area Under the Curve (AUC) of 98%. The proposed hybrid approach significantly improves over existing predictive models in thermal comfort and window state prediction.

Practical Application

This research provides a practical tool for building engineers, facility managers, and smart system developers to significantly improve energy efficiency and occupant comfort. The developed hybrid model predicts window-opening behavior with high accuracy (95%). This enables the creation of next generation BMS that can anticipate occupant needs, proactively adjust heating, ventilation, and air conditioning (HVAC) operations, and reduce unnecessary energy consumption. For building designers, the model offers data-driven understandings into realistic occupant behavior (OB), leading to better-performing natural ventilation approaches.