REAL-TIME ELECTRICAL LOAD SUPERVISION USING A SENSOR-BASED SMART MONITORING SYSTEM

Abstract

The increasing demand for energy efficiency has accelerated the development of intelligent systems capable of real-time electrical load supervision. This study presents the design and theoretical framework of a Sensor-Based Smart Load Monitoring System (SLMS), aimed at enhancing energy optimization in residential and small-scale industrial environments. The proposed system performs continuous monitoring of electrical loads, identifies anomalies in consumption, and delivers real-time notifications to users via a dedicated mobile application. The SLMS integrates current sensors, electrical measurement modules, and a wireless interface, enabling accurate data acquisition and remote feedback. In addition, the system architecture incorporates Artificial Intelligence (AI)-based analytics, allowing for intelligent pattern recognition, predictive load behavior modeling, and decision-making support. Furthermore, collected data is subject to statistical analysis, including load profiling, variance tracking, and peak usage detection, to provide actionable insights and improve overall system performance. Although the system remains at the prototyping stage, it is designed with scalability and modularity in mind, making it suitable for future deployment in smart energy platforms. Compared to traditional load monitoring systems, the SLMS introduces a real-time, user-centric, and data-driven approach, contributing to a more sustainable and responsive energy usage paradigm. The paper outlines the system design, algorithmic structure, and expected efficiency benefits, establishing a foundation for future experimental validation.