Interpretable Fuzzy Control for Energy Management in Smart Buildings Using JFML-IoT and IEEE Std 1855-2016
Source abstract
Abstract
This paper presents an interpretable and modular framework for energy management in smart buildings based on fuzzy logic and the IEEE Std 1855-2016. The proposed system builds upon the JFML-IoT library, enabling the integration and execution of fuzzy rule-based systems on resource-constrained IoT devices using a lightweight and extensible architecture. Unlike conventional data-driven controllers, this approach emphasizes semantic transparency, expert-driven control logic, and compliance with fuzzy markup standards. The system is designed to enhance both operational efficiency and user comfort through transparent and explainable decision-making. A four-layer architecture structures the system into Perception, Communication, Processing, and Application layers, supporting real-time decisions based on environmental data. The fuzzy logic rules are defined collaboratively with domain experts and encoded in Fuzzy Markup Language to ensure interoperability and formalization of expert knowledge. While adherence to IEEE Std 1855-2016 facilitates system integration and standardization, the scientific contribution lies in the deployment of an interpretable, IoT-based control system validated in real conditions. A case study is conducted in a realistic indoor environment, using temperature, humidity, illuminance, occupancy, and CO2 sensors, along with HVAC and lighting actuators. The results demonstrate that the fuzzy inference engine generates context-aware control actions aligned with expert expectations. The proposed framework also opens possibilities for incorporating user-specific preferences and adaptive comfort strategies in future developments.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: IoT-based Smart Home Systems · Currency Recognition and Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Building Construction, University of Granada, 18011 Granada, Spain
Department of Computer Science and Artificial Intelligence, University of Granada, 18011 Granada, Spain
Instituto de Investigación Biosanitaria ibs.Granada, 18011 Granada, Spain
Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18011 Granada, Spain
Department of Computer Engineering, Automation and Robotics, University of Granada, 18011 Granada, Spain
Funding
MICIU/AEI · Instituto de Salud Carlos III
References (49)
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