Hybrid AI-Driven Probabilistic Modeling of Window Operations and Thermal Comfort: A Multi-Variable Approach for Residential Environments / (Record no. 614573)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 01703nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 621 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Janjua, Osama Maqsood |
| 245 ## - TITLE STATEMENT | |
| Title | Hybrid AI-Driven Probabilistic Modeling of Window Operations and Thermal Comfort: A Multi-Variable Approach for Residential Environments / |
| Statement of responsibility, etc. | Osama Maqsood Janjua |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2025. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 109p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Occupant window interactions is a critical component in optimizing energy consumption and<br/>indoor environmental quality. Understanding the influence of environmental and behavioral<br/>factors on window state decisions remains a significant challenge in building management<br/>systems. We present an AI integrated probabilistic model to assess thermal comfort and predict<br/>the probability of the occupant opening or closing the window. The data was acquired from an<br/>open-source platform that provided yearly university dormitory window interactions. Bayesian<br/>networks and logistic regression models were applied to predict the window-opening behavior<br/>of the occupants. An average accuracy of 92% for Bayesian and 94% for Logistic regression<br/>were obtained. The results were further enhanced by combining these models through<br/>weighted methods, with weights extrapolated through generative recursive iterations<br/>generating an average accuracy of 95% and AUC of 98%. The proposed hybrid approach<br/>significantly improves over existing predictive models in thermal comfort and window state<br/>prediction. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Mechanical Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Syed Maaz Hasan |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/54603">http://10.250.8.41:8080/xmlui/handle/123456789/54603</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 08/27/2025 | 621 | SMME-TH-1155 | Thesis |