# Adaptive HVAC System Based on Fuzzy Controller Approach
## 2 Literature Review
### Existing Predictive Control  
Predictive control automates the management of HVAC operations based on dynamic indoor weather conditions or occupancy data. Predictive control uses an explicit control system modeled with different parameters to meet demand response design criteria. In other words, predictive control relies on external signals that are supplied in advance to activate demand control. One of the advantages of predictive control is that changes to the occupancy schedule do not affect the performance of the controller. The current prediction algorithm in [28; 29; 30; 31; 32] requires a control value like cost, heat demand, or photovoltaic generation to fulfill demand as cheaply as possible. This suggests that controllers must be supplied with the proper input to determine the ideal moment to use energy. Thus, the control system must use the input data to obtain the best output possible from the process. A typical example proposed in [33] uses a more advanced controller that uses inference rules frequently. The scheduling strategy often relies on precedents and practical resource restrictions to estimate job start and finish times, whereas rule-based strategies have specified membership functions and inference rules for control decision-making. Studies in [8; 34; 35; 36] use real-time occupancy information collected through various installed devices such as passive infrared, audio, carbon dioxide, and camera sensors within the perimeter of interest to detect and predict occupancy in the space. These approaches attempt to minimize the false results in occupancy prediction from the previous literature and improve appliance energy consumption. However, these approaches face challenges in predicting actual human occupancy to avoid false negatives triggered by non-human objects such as pets. Similarly, predicting the total number of occupants in the building and adjusting thermal comfort levels accordingly is another challenge. The study in [37] discusses an HVAC controller that uses carbon dioxide (CO\({}_{2}\)) and passive infrared sensors to detect occupants and send a signal to the actuator to regulate temperature depending on the number of occupants in the building. Although these sensors are widely used in the previous literature, their commercial application is not practical, as suggested by [38]. A similar approach applied CO\({}_{2}\) in conjunction with ML algorithms. The evaluation of the results indicates solid performance even with sudden changes in temperature in the perimeter of interest when space has been occupied. A study in [8; 39; 40] proposed a controller for occupancy detection using audio to effectively identify human occupancy. This controller used a Gaussian mixture model to estimate the total number of occupants in a space. The proposed method achieved a certain level of accuracy but with a high number of false results caused by nearby external sounds. Similarly, the approach required all indoor occupants to speak simultaneously to perform the occupancy estimation for HVAC ventilation control. To enhance the accuracy of the controller in [9], the study in [39] introduced a background cancelation procedure to deal with noise inference from undesirable sources by ignoring the frequency level of the desired sound frequency threshold. The idea relies on the strength of the sound frequency received by the background cancelation algorithm, which reduces false alarms by 11-12% and energy consumption by 3.54%. Despite the application of the background cancelation algorithm, the study suffers from false occupancy prediction. A controller proposed in [41] combined camera-based image and video processing with the help of computer vision (CV) libraries and estimated total occupancy numbers through headcount or indoor object tracking. A similar approach in [42] used single-camera occupancy detection to control ventilation in the lecture theater through a headcount estimation procedure. The experimental results show good performance in occupancy prediction.