000			01703nam a22001577a 4500
082			_a621
100			_aJanjua, Osama Maqsood _9130418
245			_aHybrid AI-Driven Probabilistic Modeling of Window Operations and Thermal Comfort: A Multi-Variable Approach for Residential Environments / _cOsama Maqsood Janjua
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a109p. _bSoft Copy _c30cm
500			_aOccupant 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.
650			_aMS Mechanical Engineering
700			_aSupervisor : Dr. Syed Maaz Hasan _9124968
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/54603
942			_2ddc _cTHE
999			_c614573 _d614573