## 5 Case Study: Experimental Validation in a Representative Smart Building Scenario
### Implementation of the JFML-IoT-Based Energy Management System
#### 5.4.1 Sensor and Actuator Object Instantiation
Although a detailed comparative analysis lies beyond the scope of this work, the proposed system was experimentally deployed and evaluated on two widely used embedded platforms: the ESP32 and the Raspberry Pi 3B+. These platforms are well-documented in the literature for their low energy consumption and sufficient computational capabilities for embedded inference tasks. The average inference time remained below 20 milliseconds, ensuring real-time responsiveness. Furthermore, overall resource usage was consistently low, with CPU utilization remaining under 10% and memory usage below 50% on both platforms. These results confirm the practical feasibility of the proposed solution for real-time energy management in smart building environments and pave the way for future work focused on more extensive benchmarking and performance evaluation. ## 6 Conclusions

This work presents a comprehensive framework for intelligent energy management in smart buildings based on the JFML-IoT library and compliant with the IEEE Std 1855-2016. By leveraging the modular and lightweight architecture of JFML-IoT, the proposed system enables interpretable fuzzy control logic to be deployed on low-power, embedded devices, offering a viable alternative to conventional, cloud-dependent energy management approaches. A key contribution of this work lies in the complete methodological pipeline for developing interpretable and expert-informed FRBSs, from sensor and actuator integration to the definition, validation, and formal encoding of rules in FML. The methodology is grounded in real-world requirements and expert domain knowledge, ensuring that the resulting behavior of the system aligns with human reasoning and operational expectations in dynamic indoor environments. Unlike purely data-driven control strategies, our approach prioritizes interpretability, modularity, and human-in-the-loop validation. This allows facility managers and systemdesigners to understand, adjust, and trust the system's behavior, which is essential for real-world adoption in sensitive domains such as energy and comfort regulation. The experimental case study demonstrates the practical effectiveness of the system in realistic indoor conditions. A series of representative scenarios were simulated to assess the controller's response to a diverse range of environmental situations, including variations in temperature, occupancy, illuminance, and air quality. The results confirmed that the system is capable of dynamically adjusting actuation levels for HVAC, ventilation, and lighting in a manner that is consistent with expert reasoning and energy efficiency objectives. Moreover, the system appropriately avoids unnecessary energy consumption in favorable conditions while activating targeted actions when environmental comfort or air quality demands it, validating both the interpretability and operational adequacy of the defined fuzzy rules. In terms of implementation, the system exhibited low computational load and fast response times when deployed on embedded platforms such as ESP32 and Raspberry Pi 3B+, with average inference times under 20 milliseconds and CPU usage below 10%. Although exhaustive benchmarking was not within the scope of this work, these observations reinforce the feasibility of deploying fuzzy controllers in real-time embedded IoT scenarios. Future work will focus on expanding the inference capabilities of JFML-IoT with adaptive learning techniques to support hybrid expert data-driven rule generation. Moreover, further evaluation under real-time operating constraints and larger deployment environments will be conducted to assess scalability and long-term performance. Additionally, although advanced security features such as encryption and data privacy safeguards were not explicitly implemented in this prototype, the communication layer is compatible with secure protocols like MQTT over TLS. This provides a solid foundation for incorporating authentication, encrypted messaging, and compliance with data protection regulations such as GDPR in future deployments.