IOT POWERED POWER CONSUMPTION ANALYSIS BASED ON MACHINE LEARNING ALGORITHMS

IOT POWERED POWER CONSUMPTION ANALYSIS BASED ON MACHINE LEARNING ALGORITHMS

Santhosh S, Sveda N, Vishnu A

UG Student, ECE Department

Bannari Amman Institute of Technology, Sathyamangalam

Abstract— The IoT-based power consumption analysis system utilizing the ESP32 microcontroller platform has transformed energy management through its connectivity and data exchange capabilities. This system is integral to efficient energy management and conservation. Notably, it incorporates machine learning algorithms, with Linear Regression at the forefront due to its superior accuracy compared to other models. Smart power meters linked to ESP32 microcontrollers gather real-time energy consumption data from various appliances and devices. The data is then transmitted to a centralized server for in-depth analysis. By employing advanced algorithms and machine learning, the system accurately identifies loads and recognizes consumption patterns. Through a user-friendly web interface, users gain valuable insights into their power consumption, including peak demand periods and optimization opportunities. Real-time notifications and alerts enable remote monitoring and control. Rigorous experiments in residential settings validate the system's effectiveness in precise load identification and consumption pattern analysis. The scalability, affordability, and ease of deployment of the ESP32-based system make it an invaluable tool for individuals, households, and organizations, ultimately contributing to a more sustainable and energy-efficient future