## 1 Introduction
### Fuzzy Logic-Based HVAC Control and Energy Management Systems in Other Building Settings
Furthermore, the high computational cost [36] of AI-based HVAC optimization also poses limitations in terms of scalability, especially in scenarios characterized by limited sensor installation or the use of historical data versus real-time monitoring [4; 5]. Existing demand-based control strategies have been designed for centralized HVAC systems [19; 22; 23; 30; 32; 33], with limited studies accounting for split-type ACUs, which are prevalent in educational facilities and smaller commercial buildings [30; 42]. Most demand control strategies largely rely on variations in only indoor temperatures, without taking into account the influence of fluctuations in occupancy, air quality parameters, and outdoor conditions [17; 19; 20; 26; 29; 31]. Limited studies have reported on the use of hybrid AI-based control strategies that employ rule-based decision schemes, including rough set theory (RST) and fuzzy logic, to improve the adaptability of such approaches along with simpler computation in the context of ACU setpoint optimization [6; 26]. Rough set theory can be used to generate patterns from enormous datasets that are useful and relevant [43]. Rough set theory has been proven to produce mathematically sound and computationally effective solutions to address problems relating to discovering patterns in databases, creating decision criteria, reducing data, performing principal component analysis, and conducting inference based on available data [44; 45]. Fuzzy rough set theory is a novel alternative method for transforming big data [46]. While control using fuzzy logic has been shown to improve thermal comfort along with energy efficiency, its existing formulations largely fail to enable structured rule generation along with rule validation techniques, meaning that scaling up to real conditions is problematic [11; 26]. Therefore, there is a need for adaptive and computationally simple ACU control frameworks that can dynamically adjust setpoints using multi-factorial inputs while simultaneously ensuring quick response times. In addressing these research gaps, this study develops an optimized fuzzy control framework, streamlined through the use of RST-based rule reduction, in order to improve the precision of decision-making and computational effectiveness in the context of split-type ACU control [44; 46]. Particularly, this research aims to:  
1. Identify the environmental, air quality, and occupancy parameters that contribute most to ACU power consumption using a dimension reduction technique and machine learning algorithms;
2. Develop a FIS via RST-based rule optimization, thus avoiding redundancy and facilitating real-time decision-making;
3. Evaluate the energy-saving benefits and enhancement of thermal comfort associated with this streamlined framework. Unlike the previous literature, which has focused on centralized HVAC systems, this contribution specifically addresses split-type ACUs, which are most likely to be implemented within education contexts, fulfilling a critical need within the HVAC optimization literature. The outcomes provide valuable insights for building management groups and energy management officials, offering a scalable adaptive ACU optimization framework that improves thermal comfort, reduces computational burden, and improves energy efficiency within educational and similar buildings [3; 47; 48; 49].