Hybrid AI-Driven Probabilistic Modeling of Window Operations and Thermal Comfort: A Multi-Variable Approach for Residential Environments / Osama Maqsood Janjua
Material type:
TextIslamabad : SMME- NUST; 2025Description: 109p. Soft Copy 30cmSubject(s): MS Mechanical EngineeringDDC classification: 621 Online resources: Click here to access online
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Thesis
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School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 621 (Browse shelf) | Available | SMME-TH-1155 |
Occupant window interactions is a critical component in optimizing energy consumption and
indoor environmental quality. Understanding the influence of environmental and behavioral
factors on window state decisions remains a significant challenge in building management
systems. We present an AI integrated 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 and logistic regression models were applied to predict the window-opening behavior
of the occupants. An average accuracy of 92% for Bayesian and 94% for Logistic regression
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 AUC of 98%. The proposed hybrid approach
significantly improves over existing predictive models in thermal comfort and window state
prediction.
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