The main objective of Heating, Ventilation, and Air Conditioning (HVAC) control system is to reduce energy consumption while providing comfortable indoor environment with the optimal levels. Different methods for modeling the HVAC have been discussed in this thesis. Furthermore, a comprehensive literature survey on the HVAC systems control methods has been presented. Moreover, the control approaches are classified and the benefits, drawbacks, and key features of each are extracted. The Machine Learning (ML) algorithms used in HVAC systems to control the indoor temperature and/or CO2 concentration levels of buildings while minimizing energy consumption, energy costs, power grid peak load, and providing ancillary services like frequency regulation have been reviewed. A modified model of the HVAC system has been developed and verified, and its performances are compared with those of residential load factor (RLF) model with and without the Takagi–Sugeno Fuzzy (TSF) controller. The results of the proposed model and the RLF model are compared in different ways, demonstrating that the proposed model is more efficient and stable than the RLF double cooling coil model, with energy-saving around 10.06 %. The energy consumption reduction, retaining the levels of indoor air quality and thermal comfort of the users are two significant factors to consider when evaluating the new work environment. Therefore, a novel HVAC system considering the temperature and CO2 concentration as continuous states in an integrated model has been developed using energy conservation laws and Lagrange polynomials modeling based on mass conservation law. Also, model-based (MB) Reinforcement Learning (RL) online architecture that takes optimal decisions for on/off HVAC system, lighting, open/close doors/windows system, and fresh/return air dampers ratios for creating an intelligent work environment has been presented and applied on the new HVAC system model. At each time step, the control system receiv
