Fuzzy Logic in Smart Meters to Support Operational Processes in Energy Management Systems

Distribution network operators face the complex challenge of maintaining stable electricity access for diverse consumers while balancing economic constraints, user comfort, and the impact of stochastic events, particularly the increasing integration of renewable energy sources and electric vehicles. To address these challenges, this paper introduces a novel decision-making system for energy management within smart energy meters, leveraging a specifically designed fuzzy inference system. This fuzzy inference system autonomously interprets real-time energy consumption patterns and responds to control commands from distribution network operators, optimizing energy flow at the consumer level. Unlike generic energy management approaches, this study provides a detailed mathematical model of the proposed low-cost fuzzy inference system-based system, explicitly outlining its rule base and inference mechanisms. Simulation studies conducted under varying load conditions and renewable generation profiles demonstrate the system’s effectiveness in achieving a balanced response to grid demands and user needs, yielding a quantifiable reduction in peak demand during simulated stress scenarios. Furthermore, experimental validation on resource-constrained embedded platforms confirms the practical feasibility and real-time performance of the proposed system on low-cost smart energy meter hardware. The differential contribution of this work lies in its provision of a computationally efficient and readily implementable fuzzy logic-based solution tailored for the limitations of low-cost smart energy meters, offering a viable alternative to more complex artificial intelligence algorithms. The findings underscore the necessity and justification for optimizing algorithm code for resource-constrained smart energy meter deployments to facilitate widespread adoption of advanced energy management functionalities.