Author: Sinchana, Kavyashree B, Pavithra K & Angel Deepa M.
DOI Link: https://doi.org/10.70798/Bijmrd/04042024
Abstract: Accurate billing and efficient energy management depend on electricity metering. Both digital and conventional meters mainly rely on manual procedures, which can lead to inaccurate billing, delayed data collection, human error, and a lack of consumer awareness regarding electricity usage. Both end users and meter readers are impacted by these problems. This project introduces an Internet of Things (IoT)-based smart electricity meter that incorporates machine learning techniques to address these shortcomings. The system continuously measures voltage, current, and power consumption using embedded sensors and transmits real-time data via wireless communication to a cloud platform.Machine learning models identify anomalous energy behavior, forecast future consumption, examine usage trends, and offer astute suggestions for effective power use. All things considered, the suggested solution boosts energy efficiency, facilitates deployment, increases billing accuracy, and improves transparency. This project demonstrates how IoT and ML technologies can transform conventional electricity metering into an intelligent,automated, and usercentric energy management solution.This strategy encourages consumer to use energy responsibly and lowers operating costs for utilities.All things considered,the solution supports sustainable and effective electricity management and is in line with smart grid goals. In order to guarantee data integrity, secure communication protocols are used Furthermore,the suggested system eliminates the need of physical meter inspections by enabling automated data logging and remote meter reading.
Keywords: Energy Management, IoT, Embedded Sensors, and Machine Learning Models.
Page No: 196-200