Artificial Intelligence & Machine Learning Based Smart Energy Meter
Pranith Kumar D S1 R Sandeepa2
Department of electronics and Communication, Department of electronics and Communication,
Jawaharlal Nehru New College of Engineering, Jawaharlal Nehru New College of Engineering,
Karnataka, 577201 Karnataka, 577201
pranithkumards@gmail.com rsandeepsys@gmail.com
Prajwal G M3 Punith S4
Department of electronics and Communication, Department of electronics and Communication,
Jawaharlal Nehru New College of Engineering, Jawaharlal Nehru New College of Engineering,
Karnataka, 577201 Karnataka, 577201
Prajwalprajwalgm941@gmail.com punithgowda50997@gmail.com
Darshan K V5
Department of electronics and Communication,
Jawaharlal Nehru New College of Engineering,
Karnataka, 577201 darshankv@jnnce.ac.in
Abstract: Energy management is an inspiring domain in developing of renewable energy sources. The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization, minimize energy costs without affecting production, and minimize environmental effects. The rapid growth in energy demand and the increasing integration of renewable energy sources have created significant challenges in efficient power management and consumption optimization. Traditional energy metering systems lack real- time intelligence, adaptability, and predictive capabilities, resulting in energy wastage and inefficient load management. To address these limitations, this paper presents an Artificial Intelligence and Machine Learning–based Smart Energy Management System designed to monitor, analyze, and optimize energy consumption patterns in real time.
The proposed system utilizes smart sensors, Machine learning algorithms such as linear regression, decision trees to forecast energy demand, detect anomalies, and identify consumption trends, Artificial intelligence techniques enable automated decision-making. Experimental results demonstrate the integration of AI and ML provides scalability and adaptability, making the system suitable for residential, commercial, and industrial applications.
