## 2 Literature Review  
In the literature, many control methods have been proposed and developed for HVAC systems. These methods can be divided into classical control, hard control, soft control, hybrid control, and other control techniques. Details of these methods are compared and discussed in [11; 12]. These methods can also be divided into traditional, advanced, and intelligent [12]. In [13; 14], an overview of the advanced control strategies for HVAC is presented. A comprehensive review discussing computational intelligence techniques for HVAC systems is presented in [2]. It is worth mentioning that classical control methods, including on-off (bang-bang) and proportional-integral-derivative (PID), are still used in many HVAC systems because of their simplicity and low cost [11; 12]. Nowadays, the concept of the Internet of Things is often used to improve control of HVAC systems. The term _Internet of Things_ was first proposed by [PERSON] in [15] as a network of physical objects embedded with electronics, circuits, software, sensors, and network connectivity that enables these objects to collect and exchange data. This has found many applications in the control of the state of systems for which the state is dependent on many variables [16]. One of the applications of IoT is the identification of human thermal comfort, which can be approximated by the Predicted Mean Vote [17]. Environmental variables (temperature, air velocity, and relative humidity) are measured by sensors (within a greater system) that are able to determine what thermal sensations a person experiences. The calculated indicator can be used to automatically control air conditioning. This system can improve comfort and reduce the energy consumption at the same time [7]. While the Predictive Mean Vote approach relies on an aggregated comfort model, other models consider distinct preferences between unique individuals [18]. Data collected by a set of sensors, occupants' feedback from wearables, an online survey that gathered occupant-related information about age, gender, and clothing level, allows for the development of personal comfort models. Personalized thermal comfort models are able to capture the distinct preferences of each individual to enable a better understanding of the specific comfort requirements inside a building. Occupant behavior, especially inter actions with building systems like HVAC, impact the building energy performance [19]. Wireless technologies like Wi-Fi or Bluetooth, image recognition, sound detection, passive infrared detection systems, and sensor fusion might capture behavior patterns and provide information about thermal-zone-level occupant localization. Occupancy-based HVAC control strategies can be classified as either user-defined schedules or occupancy-based mechanisms. In the former, occupants use manual or programmable thermostats or other means of scheduled control. In the latter, occupancy detection and monitoring systems provide input to reactive or predictive control, which can either be defined by a set of rules or driven by optimal control algorithms [20; 21]. Control algorithms can be divided into [22]: local control, responsible for adjusting actuators of the system according to the predefined set points (e.g., on-off, PID), and high-level control, where an additional layer of control determines set points of the local control (e.g., predictive control, machine learning, and other artificial intelligence algorithms). Development of IoT data-driven closed-loop control systems in residential buildings faces many challenges related to data acquisition, transfer, storage, aggregation, and processing to deliver useful information for an HVAC system [10]. In [23], metrics and examples of testing environments for HVAC systems are presented. In laboratory conditions, it is possible to simulate the test environment using an electrical circuit composed of resistors and capacitors, which corresponds to the resistance and heat capacity of elements of a room (mainly air and walls) [24].