# Adaptive HVAC System Based on Fuzzy Controller Approach
###### Abstract  
Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.  
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occupant comfort [3; 17], while ANN and AC provide solutions to optimize energy usage performance during periods of extreme heat [18; 19; 20] while maintaining acceptable indoor comfort levels.  
Most of these studies use ambient temperature and humidity to control indoor comfort and energy consumption [21; 22; 23]. Studies in [24; 25] argue that ambient temperature and humidity cannot fully or precisely reflect the occupant's thermal comfort sensation in light of other parameters. Consequently, this study modified IoFClime [26] to implement occupancy-based controls to deal with the aforementioned challenge and achieve high energy savings potential. IoFClime is an HVAC-based controller that implements fuzzy logic to manage room temperature while taking into consideration outdoor conditions.  
A fuzzy inference engine was used to model inputs with a specified number of defined rules to keep the indoor temperature consistent with the desired comfort level under three possible scenarios. A feedback loop was included to deal with changes in the desired comfort level, and a real-time electricity tariff was used to evaluate the proposed approach against energy consumption and thermal comfort levels.  
The proposed approach can reduce energy consumption by up to 50% when occupants choose to sacrifice thermal comfort for energy savings, in comparison with the study in [26] (IoFClime). The proposed fuzzy controller has the following key contributions:  
* The interface allows the occupant to set the desired setpoint on the thermostat.
* Depending on the desired setpoint, the controller picks up and regulates energy consumption within the range of the occupant's desired thermal comfort zone.
* Finally, the proposed approach can be utilized to take on additional parameters.  
This research is organized as follows: The literature review in Section 2 presents analyses of the recent smart HVAC controller. The problem formulation is presented in Section 3. An overview of system implementation is presented in Section 4. The study results are presented in Section 5. Finally, Section 6 concludes the study.