## 1 Introduction
### Fuzzy Logic-Based HVAC Control and Energy Management Systems in Other Building Settings  
Table 4 summarizes the applications of fuzzy logic in HVAC and energy management systems across various building types other than educational settings, highlighting both achievements in energy efficiency and limitations in broader implementation. In [33], machine-to-machine (M2M) communication was integrated with fuzzy logic to optimize energy use, achieving energy savings of 16% while maintaining occupant comfort improvements of up to 17%. However, the study acknowledged scalability challenges, particularly in larger or more complex buildings where sensor network reliability and computational overhead become concerns. In [34], a fuzzy logic-based mobile app that utilized occupant feedback to dynamically adjust HVAC parameters was developed. The scheme saved 7.4-13.2% of energy, but its validation only extended to single-occupancy situations, making its applicability uncertain for multi-occupancy areas of interest. Correspondingly, in [35], fuzzy PID control combined with EnergyPlus simulation was utilized to achieve 4-14% energy reductions through occupancy-based management of energy consumption. The use of estimation-based modeling compared to real-world deployment also indicated that there is a certain scope for using real sensor-based real-time data for more accurate validation. A more behavioral approach was taken in [36], employing fuzzy linguistic rules to enable behavior-based management of energy, considering inputs such as outdoor temperatures, zone conditions, and the operation of cooling units. While the study incorporated efficiencies, its extension to diverse building typologies was problematic due to the need to significantly customize the rule-based models in order to generalize to diverse climate conditions and occupancies. The studies summarized in Tables 1-4 have demonstrated that the operational parameters of ACUs can be optimized through fuzzy logic-based control strategies and energy-saving measures. However, most studies have reported either on experimental \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Methods Used** & **Input Variables** & \begin{tabular}{c} **Energy Savings** \\ **[\%]** \\ \end{tabular} & \
\begin{tabular}{c} **Study** \\ **Period** \\ \end{tabular} &
\begin{tabular}{c} **Other** \\ **Techniques Used** \\ \end{tabular} & **Room Type** \\ \hline
[39] & Fuzzy logic & \
\begin{tabular}{c} Load, lighting, \\ with M2M \\ Temp., clothing \\ (clo), thermal \\ sensation \\ \end{tabular} &
\begin{tabular}{c} 16 (M2M), \\ 14 (Enable/Disable) \\ \end{tabular} & — & None & 3D office \\
[24] & Fuzzy logic &
\begin{tabular}{c} (clo), thermal \\ sensation \\ \end{tabular} & 7.4 to 13.2 & — & Mobile app & Building \\
[40] & EnergyPlus, \begin{tabular}{c} Energy PID \\ \end{tabular} & \
\begin{tabular}{c} Occupancy, error \\ (eR), error change \\ (\(\Delta\)eR) \\ \end{tabular} & 4 to 14 & 2 years &
\begin{tabular}{c} Estimation \\ models \\ \end{tabular} & Building \\
[41] & Fuzzy linguistic rules &
\begin{tabular}{c} Outdoor temp., \\ zone temp., \\ chiller temp. \\ \end{tabular} & — & — & GUI for behavior display & Office building \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research papers on fuzzy logic-based HVAC control in buildings. tests of ACU performance [30; 31; 32; 37; 38] or AI-based optimization of HVAC systems, presenting certain limitations [27; 28; 39]; in particular, the applicability of AI-based HVAC models is restricted by their applicability to building typologies [9; 26; 29; 30; 31; 32; 33; 33] or levels of occupancy [9; 34; 40]. Some of the existing models employ pre-determined data [35] and environmental conditions, thus requiring extensive validation before they can be applied to real-world scenarios [2; 41].