# Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning
## 2 Related Work  
The indoor temperature of a building is often considered to be the main parameter in air-conditioning systems. It has a big impact on energy consumption. An increase or decrease by just \(1\,^{\circ}\mathrm{C}\) can mean that the energy consumption from the cooling and heating can be changed by 5-10% [18]. The simplest control system that can be used for energy conservation is based on a scheduled time strategy for switching on or off a heating system [19; 20; 21; 22]. An occupant's thermal comfort should ideally be adaptively maintained by changing the temperature depending on the activity of the occupant(s). Many researchers have shown that PMV can recognise and help to maintain thermal comfort levels. A further advantage is that there can be improvements in energy conservation when compared with conventional control systems [23; 24]. A study that used the PMV model to quantitatively describe the thermal comfort levels of occupants can be found in [18]. A PMV model was used in a fuzzy control strategy to maintain occupants' thermal comfort. The PMV was considered as the set point of the controller, whilst feedback was obtained from the PMV calculation based on temperature and humidity variables. Subsequently, the difference error was fed as Fuzzy controller  
Figure 1: Fuzzy temperature control based on occupant action and the PMV index for thermal comfort. input. The experimental results showed that the model achieved energy savings with the indoor temperature variation according to the ambient conditions whilst maintaining the thermal comfort level. The PMV model was also used by [25] to control and monitor the indoor thermal conditions. This work considered outdoor weather conditions to be explicitly included in the solution. A fuzzy logic controller was used to control the heating system. Two inputs were considered: the PMV index value and outdoor weather readings. The system was tested and compared with a classical Proportional-Integral-Derivative (PID) controller. The results showed that the proposed method achieved better performance than the PID controller. An optimal balance between occupants' thermal comfort and energy consumption was considered by [13]. Different parameters for an indoor room were measured, such as temperature, humidity, heat, and other subjective variables. PMV was used to calculate the thermal comfort level using measured parameters. A genetic algorithm was used to obtain optimum comfort temperatures to help reduce energy consumption. The genetic algorithm was shown to improve the system and reduce power consumption. In [11], the PMV model was used for energy conservation and to help maintain the amenity index of indoor environments at a preferred level. A PMV prediction control method was proposed to maintain the PMV value within a preset range. This helped to reduce power consumption. The study also considered other parameters including room volume, window size and the number of people. The proposed method was evaluated in an office building in Japan. The experiments showed that the proposed method maintained the comfort level of the room at the desired level. A sensing system was proposed by [12] for thermal comfort and indoor air quality parameter monitoring. It also correlated the output of a PMV model with air quality factors, including the levels of carbon monoxide and carbon dioxide. Tests were performed using 50 subjects over a period of 10 days. The results showed good performance of the proposed system; this was confirmed with the use of Thermal Sensation Vote (TSV). All of these methods did not vary the metabolic rate of the occupants. The metabolic rate was assumed to be fixed, despite it typically varying as a function of a person's activity. In [26; 27], a heating control system was proposed to improve the trade-off between environmental comfort and energy conservation. Mobile phone devices were proposed to be used to measure occupants' metabolic rates. This was then used in the calculation of a PMV thermal comfort level.