AI Based Smart Energy Saving and Consumption Management for Industries

This paper introduces an intelligent energy management system (EMS) designed to enhance energy efficiency in office, academic, and industrial environments through real-time human presence detection and IoT-based monitoring. Unlike conventional motion sensors, the proposed system employs image processing and machine learning algorithms implemented on a Raspberry Pi platform to accurately and contactless identify occupants. Based on detected occupancy, the system dynamically controls connected electrical loads via Arduino-based relays, minimizing unnecessary power usage and improving operational efficiency. In parallel, a real-time three-phase monitoring module was developed using PZEM-004T sensors and ESP8266 microcontrollers to measure voltage, current, and power factor across industrial networks. The data is processed locally and uploaded to a cloud dashboard for visualization, fault detection, and maintenance planning. Experimental validation demonstrates that both systems operate reliably under varying load conditions and significantly reduce energy wastage. The proposed dual-module approach—combining intelligent occupancy-based control with IoT-enabled monitoring—provides a cost-effective, scalable, and practical solution for modern energy management. It not only improves efficiency and safety but also establishes a foundation for future integration with renewable energy systems, predictive analytics, and privacy-aware smart building architectures aimed at promoting sustainability in industrial and institutional settings.