Physics Inspired Machine Learning Simulator for Heating and Insulation Effects on Houses / (Record no. 615188)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02123nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 629.8 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Saleem, Muhammad Awais |
| 245 ## - TITLE STATEMENT | |
| Title | Physics Inspired Machine Learning Simulator for Heating and Insulation Effects on Houses / |
| Statement of responsibility, etc. | Muhammad Awais Saleem |
| 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 | 102p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Over 40% of all primary energy use comes from buildings, which also has the potential to provide the grid with some degree of energy flexibility. HVAC systems<br/>are crucial for ensuring a comfortable indoor environment for building occupants.<br/>While classical white-box models must adhere to the laws of physics, their accuracy may be limited by their inherent structure. Additionally, they have significant<br/>computational overhead, making them less practical for testing control systems.<br/>Several studies have utilized black-box models to develop simulators that forecast<br/>the indoor environment, but these studies typically focus on short-term prediction horizons of up to 1 week. Furthermore, few studies have accounted for the<br/>uncertainties in weather conditions, which can significantly impact the indoor environment and lead to energy wastage in HVAC systems. In this paper, we propose<br/>the development of a fast and accurate simulator for a multi-zone dwelling that<br/>can forecast the indoor environment for 6 months while incorporating weather<br/>uncertainties. We used 9 machine learning models namely, LR, Lasso, Ridge,<br/>Elastic Net, Extra trees, Regression Trees, Random Forest, KNN, and XGBoost<br/>xviii<br/>and 1 deep learning model namely, MLP. Hyperparameters are optimized for the<br/>machine learning models, and the indoor environment is forecasted. Among the<br/>models implemented, Lasso regression emerged as the best model with an RMSE<br/>of 0.077 for a 3-month forecast while KNN has an RMSE of 0.09 for 6-months<br/>prediction. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Robotics and Intelligent Machine Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Khawaja Fahad Iqbal |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/55786">http://10.250.8.41:8080/xmlui/handle/123456789/55786</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 | 10/15/2025 | 629.8 | SMME-TH-1191 | Thesis |