FUW TRENDS IN SCIENCE & TECHNOLOGY JOURNAL

(A Peer Review Journal)
e–ISSN: 2408–5162; p–ISSN: 2048–5170

FUW TRENDS IN SCIENCE & TECHNOLOGY JOURNAL

Design and Implementation of IOT-enabled Meter for Predictive Energy Management using Machine Learning
Pages: 64-71
Alexander A. Okandeji1, Ayodeji A. Okubanjo2, Alao O. Peter2, Adeoye A. Lutfat


keywords: IoT, smart metering, machine learning, energy demand,

Abstract

In the era of digital transformation, internet of things IoT based smart metering systems represent a pivotal advancement in energy management and monitoring. This paper introduces an innovative smart metering system leveraging machine learning models to predict yearly energy demand and cost. By integrating IoT sensors with advanced data analytics, the system offers real-time monitoring and predictive capabilities, optimizing energy consumption for both consumers and utility providers. Through the analysis of historical usage patterns and environmental factors, such as weather conditions and population dynamics, the machine learning models accurately predict future energy demand, empowering consumers to make informed decisions and proactively address energy challenges. The fusion of IoT and machine learning holds the promise of revolutionizing energy management, with new technologies fostering a greener, more resilient planet for generations to come.

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