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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
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
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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.