ML-enabled IoT-driven smart meter data analysis for precise energy consumption prediction

K. Kumaraswamy; Viramallu Sri Varshini; Satnoor Rohini Swamy; Siripuram Ruthvika

Author(s)

K. Kumaraswamy Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana. India

Viramallu Sri Varshini Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana. India

Satnoor Rohini Swamy Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana. India

Siripuram Ruthvika Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana. India

Abstract

Countries are placing a greater emphasis on energy efficiency in an effort to reduce their carbon emissions and make the most of the resources they have available inside their borders. The tracking of energy use in the past consisted of either hand-reading meters or crude automated devices that provided only a limited amount of data. It is common for conventional energy consumption prediction systems to rely on too simplistic models that do not make full use of the opportunities presented by data from smart meters. In most cases, these systems have problems with accuracy and scalability, particularly when dealing with large datasets and complex consumption patterns. In traditional energy management systems, the majority of the time, the process of predicting energy consumption is carried out by predetermined algorithms or statistical methods. As a result of their inability to adapt to changing patterns of usage or to take into account anomalies, these methods typically result in inaccurate estimates and the wasteful consumption of energy. Because they are unable to process enormous amounts of data and because they do not have the capability to adjust in real time, their effectiveness is restricted. For the purpose of overcoming these limitations, the research employs powerful machine learning algorithms to examine data from smart meters that are driven by the Internet of Things. Machine learning algorithms make it feasible to improve energy management approaches, better anomaly identification, and provide more accurate forecasts. All of these improvements are made possible by the application of machine learning. In order to achieve the objectives of enhancing energy efficiency, cutting expenses, and supporting sustainable energy practices, this program offers precise and data-driven insights into patterns of energy consumption.

Publication Details

Page(s) 630-637

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 05, Year: 2024

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Keywords

energy efficiency, Internet Of Things (Iot), Machine learning algorithms, carbon emissions, Energy consumption prediction, Smart meters

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