## 1 Introduction  
Air conditioning units (ACUs) provide environmental comfort and quality in residential and commercial buildings. However, the high energy requirements associated with their operation lead to a significant increase in overall consumption. With environmental and energy efficiency concerns having been raised, the optimization of the performance of ACUs has rapidly developed as a field of research. A series of research works regarding the management and monitoring of power consumption with the aid of Internet of Things (IoT) sensors has been published, and a practical and innovative approach for automation to increase the efficiency of buildings through the use of sensors has been highlighted in [1, 2, 3, 4]. Fuzzy logic can be employed in the realm of decision-making for the control of systems that are characterized by imprecision, ambiguity, or vagueness. This approach simulates human thinking processes, utilizing linguistic concepts and norms to effectively navigate intricate and unpredictable scenarios. Fuzzy logic has been used in several domains, encompassing control systems for temperature and humidity control in buildings [5, 6, 7, 8, 9], plant greenhouses using IoT and wireless sensor technologies [10; 11; 12], monitoring of environmental conditions [13], the combination of air quality and thermal comfort indicators [14], and medical incubators [15]. It also has significant applications in automotive engine control, pattern identification tasks (e.g., picture and speech recognition), decision support systems, and natural language processing. This technology is generally employed for the purpose of designing systems that can effectively function in contexts characterized by uncertainty or dynamism. Table 1 summarizes the general application of fuzzy logic in HVAC control systems, focusing on fuzzy logic-based and hybrid methods that aim to enhance energy efficiency and thermal comfort. In [16; 17], fuzzy logic integrated with machine learning models was employed for the optimization of thermal comfort through real-time data monitoring. Although these approaches showed promise in terms of their dynamic adaptability, they lacked specific performance metrics, limiting the quantifiable assessment of their impacts. [PERSON] et al. (2021) utilized a mobile app integrated with fuzzy logic to personalize HVAC settings based on occupant-specific feedback. This approach achieved energy savings of 7.4-13.2% while demonstrating the potential for individualized thermal comfort control, although scalability to multi-occupant environments remains a challenge. In [18; 19], fuzzy logic was leveraged for direct load control and energy-saving HVAC systems, reporting energy savings of up to 37%. However, these studies identified limitations in the adaptability of their systems to varying operational conditions, which could impact the consistency of their performance. In [20], neuro-fuzzy modeling--combining fuzzy logic with machine learning--was utilized to precisely regulate thermal comfort. This approach achieved energy savings between 8.5% and 18%, with the added benefit of fine-tuned comfort levels, highlighting its effectiveness in environments with complex thermal dynamics. Other contributions, such as [21; 22], have integrated fuzzy control with mobile applications and predicted mean vote (PMV)-based feedback mechanisms, enhancing user interactions and improving thermal comfort efficiency. These studies showcased the importance of incorporating occupant behaviors into HVAC control strategies. The optimization strategies reported in [23] further advanced fuzzy logic applications through their combination with algorithms such as dynamic extreme learning machines (DELMs) and bat optimization. This hybrid approach was shown to significantly reduce energy peaks and offers a robust solution for peak load management.