000			02123nam a22001577a 4500
082			_a629.8
100			_aSaleem, Muhammad Awais _9131195
245			_aPhysics Inspired Machine Learning Simulator for Heating and Insulation Effects on Houses / _cMuhammad Awais Saleem
264			_aIslamabad : _bSMME- NUST; _c2025.
300			_a102p. _bSoft Copy _c30cm
500			_aOver 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 are crucial for ensuring a comfortable indoor environment for building occupants. 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 computational overhead, making them less practical for testing control systems. Several studies have utilized black-box models to develop simulators that forecast 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 uncertainties in weather conditions, which can significantly impact the indoor environment and lead to energy wastage in HVAC systems. In this paper, we propose the development of a fast and accurate simulator for a multi-zone dwelling that can forecast the indoor environment for 6 months while incorporating weather uncertainties. We used 9 machine learning models namely, LR, Lasso, Ridge, Elastic Net, Extra trees, Regression Trees, Random Forest, KNN, and XGBoost xviii and 1 deep learning model namely, MLP. Hyperparameters are optimized for the machine learning models, and the indoor environment is forecasted. Among the models implemented, Lasso regression emerged as the best model with an RMSE of 0.077 for a 3-month forecast while KNN has an RMSE of 0.09 for 6-months prediction.
650			_aMS Robotics and Intelligent Machine Engineering _9119486
700			_aSupervisor : Dr. Khawaja Fahad Iqbal _9125661
856			_uhttp://10.250.8.41:8080/xmlui/handle/123456789/55786
942			_2ddc _cTHE
999			_c615188 _d615188