## 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
Another promising direction for future research involves integrating the proposed fuzzy control framework with Building Information Modeling (BIM) platforms or Digital Twin technologies. These models offer rich semantic and structural representations of the building environment that could complement our edge-oriented, rule-based approach, enabling context-aware and coordinated energy control strategies that bridge long-term planning with real-time decision making. The presented system constitutes a solid and extensible foundation for human-interpretable control in smart buildings, paving the way toward more transparent, adaptive, and trustworthy energy management infrastructures. ### Limitations and Critical Points

While the proposed system has demonstrated promising results in terms of interpretability and operational feasibility, several limitations must be acknowledged. First, the current implementation was validated in a controlled, small-scale environment using predefined fuzzy rule sets. This setup does not fully reflect the variability, unpredictability, and complexity of large-scale or long-term building operation scenarios. Additionally, although the fuzzy rule base was developed through a structured expert elicitation process, the system does not yet incorporate mechanisms for automatic rule refinement, online adaptation, or data-driven learning. As a result, its responsiveness may be constrained in environments with evolving usage patterns or unexpected behaviors. Another limitation lies in the validation process itself, which was conducted using representative but static scenarios. This restricts the generalizability of the results and calls for future empirical studies involving continuous monitoring under dynamic, real-world conditions. Furthermore, the current system lacks advanced security features--such as encrypted communication channels and authentication protocols--which would be essential for safe deployment in production environments. The rule base design was intentionally focused on the perspective of technical domain experts (e.g., HVAC engineers, building automation specialists), without incorporating direct input from end-users. While this ensures context-aware and safe control logic, it may limit the system's ability to personalize comfort settings or adapt to individual behavioral preferences. This could affect user acceptance and optimal calibration in heterogeneous occupancy scenarios. Future extensions should explore hybrid strategies that integrate expert knowledge with end-user feedback or adaptive learning to enhance personalization and scalability. Moreover, the current study did not quantify the energy savings or environmental impact (e.g., CO\({}_{2}\) reduction) attributable to the fuzzy control system. Although the experimental setup enables energy-efficient operation in principle, the absence of detailed subsystem-level consumption data (HVAC, lighting, ventilation) precludes a rigorous assessment of performance gains. Similarly, no comfort metrics (e.g., Predicted Mean Vote index) were recorded, limiting the comprehensiveness of the evaluation. These gaps highlight key directions for future research. Longitudinal deployments should include subsystem-level energy monitoring, comparison with baseline control systems, and modeling of environmental benefits based on regional electricity carbon factors. In parallel, a techno-economic analysis encompassing hardware costs, installation, maintenance, and potential disruptions (e.g., power outages) would allow for estimation of payback periods across different deployment scales. These efforts are critical to inform the cost-benefit profile and real-world adoption potential of the proposed architecture. Conceptualization, M.M.-R., C.C., J.A.-F. and J.M.S.-H.; methodology, J.A.-F. and J.M.S.-H.; software, C.C. and J.M.S.-H.; validation, M.M.-R. and J.A.-F.; formal analysis, M.M.