## 1 Introduction
### Fuzzy Logic-Based HVAC Control Systems Used in Educational Buildings  
The studies shown in Table 2 (sorted from the most recent to the oldest) reflect the application of fuzzy logic-based control systems in educational buildings, classrooms, and laboratories, where critical input parameters such as temperature, humidity, CO\({}_{2}\) levels, and occupancy have significant effects on HVAC energy consumption. While fuzzy logic is well-established as a dynamic HVAC control method, challenges may be experienced while implementing this process within educational buildings. These challenges are related to fluctuating occupancy levels, uncertain thermal loads, and the need for the system to possess rapid adaptability to ensure energy efficiency along with thermal comfort. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Methods Used** & **Input Variables** & **Energy Savings** & **Fuzzy Rules** & **Study Period** & **Other Techniques Used** \\ \hline
[19] & Fuzzy PI-PD, T-S controllers & Cooling load, damper, fan speed & \(>\)37\% & — & — & Gradient descent \\
[20] & Neuro-fuzzy modeling & Indoor temperature, humidity & 8.5–18\% & 33 & — & Temp. difference module \\
[21] & Fuzzy logic with mobile app & Temperature, humidity & 0.4 & 5 & — & MIT App, Arduino \\
[22] & PMV feedback & PMV, air velocity, temperature, humidity & 0.3 & — & 4.5 h & ANFIS, particle swarm \\
[25] & Fuzzy logic, PID controllers & System error, error & — & — & Neural networks for comfort \\
[23] & DELM, Bat & Temp., illumination, CO\({}_{2}\) & Reduced peak from 29 Wh to 18 Wh & — & — & Optimization algorithms \\
[26] & Fuzzy controller & Temp., temp. diff. & — & — & — & Comparison with PID, On/Off \\
[27] & Fuzzy-HMOGA & Temp. error, error diff. & — & — & — & Multi-agent systems \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Methods Used** & **Input Variables** & **Energy Savings [\%]** & **Study Period** & **Techniques** & **Room Type** \\ \hline
[9] & Fuzzy inference, occupancy sensors & PMV, distance, environment & — & 2 months & Infrared sensors & Small Room \\
[28] & PLC, fuzzy logic & Temperature, time, occupancy & — & 3 days & Sliding control & Multi-Room \\
[29] & PID + fuzzy logic & measurements & 17 & 1 day & Comfort sensors & Office \\
[30] & Simulation of fuzzy controllers & Temperature, CO\({}_{2}\), lighting & 20 HVAC, 76 lighting & 10 days & Sibil Model & Laboratory \\
[31] & Fuzzy with physiological data & Temperature, CO\({}_{2}\), heart rate & 20 & — & Regression & Office \\
[32] & CFD, YOLOv5, simulation & Students, thermal parameters & — & — & Machine learning & Classroom \\
[33] & Fuzzy logic & PMV, outdoor weather & — & 4 months & None & Workspace \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Research papers on fuzzy logic-based HVAC control in small rooms. One of the most critical challenges is the effective utilization of occupancy-based fuzzy logic control within laboratories and classrooms, which are characterized by very dynamic occupancy. Traditional fuzzy logic controllers have utilized occupancy sensors to modulate the operation of ACUs based on real-time sensing of people occupying spaces. However, misdetections of people occupying spaces by these control systems have been reported, especially in cases where students remain seated for extended periods of time [9]. Such situations can lead to the false identification of unoccupied rooms, resulting in unnecessary reductions of cooling output and impacting occupant comfort accordingly. Furthermore, multi-room fuzzy control, including when implemented within programmable logic controllers (PLCs), has been explored to modulate ACU setpoints within multi-room contexts [24]. While control with greater accuracy can be achieved using such control systems, some practical challenges remain, including system faults, high computational demand, and complex maintenance activities.