## 1 Introduction
Unlike conventional control mechanisms that rely on precise mathematical models and deterministic decision-making, fuzzy inference systems (FISs) allow for nuanced, human-like reasoning that can adapt to real-world variability and approximate solutions in uncertain conditions [19]. By leveraging linguistic variables and membership functions, FISs enable energy management strategies that incorporate factors such as occupant behavior, external environmental conditions, and device performance with greater flexibility. This adaptability makes fuzzy logic particularly suited for optimizing energy efficiency in buildings, where diverse factors interact in non-linear and unpredictable ways [20]. Recognizing the potential of fuzzy systems for intelligent decision-making, the norm IEEE std 1855-2016 was introduced to formalize the development of interoperable fuzzy systems [21]. This standard defines a structured methodology for designing, implementing, and integrating fuzzy logic-based models, ensuring consistency across different hardware and software platforms. A key advancement stemming from this standard is the development of the Java Fuzzy Markup Language (JFML) library, an open-source framework that facilitates the implementation and deployment of fuzzy systems across various domains, including industrial automation, healthcare, and IoT applications [22]. Recent research has extended JFML to IoT environments, enabling its application in distributed sensor networks [23]. The integration of FISs with IoT allows for the real-time adaptation of energy management strategies by processing large volumes of sensor data with minimal computational overhead. However, the current version of JFML-IoT remains limited in its support for low-power devices and diverse sensor-actuator configurations, restricting its applicability in resource-constrained environments such as energy management systems in smart buildings. Building upon these foundations, the present work aims to validate how an interpretable and expert-informed fuzzy control system--compliant with IEEE Std 1855-2016--can be effectively deployed to optimize energy usage in smart buildings. The scientific contribution lies in demonstrating the applicability of a modular framework capable of real-time control of HVAC, lighting, and ventilation systems using transparent and adaptable logic. While the fuzzy rules have been collaboratively designed with domain experts, they were conceived to reflect occupant needs and common usage scenarios, thus supporting both energy efficiency and indoor comfort. Furthermore, this framework establishes a basis for future extensions that incorporate user-specific preferences and direct feedback, strengthening the connection between interpretability and occupant-centered control. The remainder of this paper is structured as follows. Section 2 presents an overview of related work, including existing research on IoT-based energy management, AI and fuzzy logic applications in smart buildings, and relevant control strategies for lighting, ventilation, and occupancy-based energy optimization. Section 3 introduces key preliminary concepts, providing a foundation for understanding fuzzy logic, Fuzzy Rule-Based Systems (FRBSs), the IEEE Std 1855-2016, and the main features of the JFML library. Section 4 details the architecture of the proposed system, describing the integration of sensors, actuators, and the fuzzy inference model for energy optimization. Section 5 presents a case study evaluating the framework in a real-world environment, demonstrating its effectiveness in adaptive energy management. Finally, Section 6 concludes the paper, summarizing key findings and outlining future research directions.