Perspective-smart energy management system using machine learning - review article

Aug 2, 2026

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Rising energy demand and the need for sustainability drive the development of smart energy management systems (SEMS). Machine learning (ML) strengthens SEMS by enabling data-driven demand prediction, adaptive control, and real-time decision-making. The review covers current SEMS that use ML to optimize energy consumption, improve grid reliability, reduce waste, and lower costs. It also analyzes key components, practical applications, persistent challenges such as data quality and scalability, and future prospects for integrating renewable energy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Introduction

Demand forecasting with machine learning

Grid management with ML

Optimization of energy consumption

Benefits: efficiency, cost savings, sustainability

Challenges and outlook

Perspective-smart energy management system using machine learning Vinod Balmiki 1, SanthoshKumar.C2, Praveen Sharma3, Laith H. A. Fezaa4, MuntatherAlmusawi5, and M.Lakumanan6 *Department of Civil Engineering, Uttaranchal Institute of Technology, Uttaranchal University,Dehradun-248007, India †Assistant Professor,Department of CIVIL,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai-127 ‡Department of Computer Science &Engineering, IES University, IES College of Technology,Bhopal, MP 462044 India. §Department of Optical Techniques, Al-Zahrawi University College, Karbala, Iraq 5 **The Islamic university, Najaf, Iraq 6Assistant professor, Department of Data Science, K.S.Rangasamy College of Arts and Science (Autonomous), Tiruchengode, Mail [id : m.lakumanan@ksrcas.edu](id : m.lakumanan@ksrcas.edu) Abstract.In today's rapidly evolving world, the demand for energy is steadily increasing, while the need for sustainability and efficient resource utilization becomes ever more critical. Smart Energy Management Systems (SEMS) are poised to play a pivotal role in addressing these challenges. Leveraging the power of Machine Learning (ML), SEMS offer a promising avenue to optimize energy consumption, enhance grid reliability, and reduce carbon footprints. This review article provides an in-depth exploration of the current state of Smart Energy Management Systems empowered by Machine Learning, highlighting their key components, applications, challenges, and future prospects. Keywords: Machine Learning, Smart Home, Energy Management. *[Corresponding Authour](Corresponding Authour :vinodbalmiki111@gmail.com)[ :](Corresponding Authour :vinodbalmiki111@gmail.com)[vinodbalmiki111@gmail.com](Corresponding Authour :vinodbalmiki111@gmail.com) †[c.santhoshkumar_civil@psvpec.in](c.santhoshkumar_civil@psvpec.in) ‡[research@iesbpl.ac.in](research@iesbpl.ac.in) §[laithfizaa@gmail.com](laithfizaa@gmail.com) ** [muntatheralmusawi@gmail.com](muntatheralmusawi@gmail.com) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). 1. Introduction As the world grapples with the dual challenges of increasing energy demand and the urgent need to address environmental issues, the development of Smart Energy Management Systems (SEMS) has emerged as a critical solution. These systems are designed to intelligently control and optimize energy resources, ensuring that energy is generated, distributed, and consumed in the most efficient and sustainable manner possible. At the heart of this transformation lies Machine Learning (ML), a powerful tool that enables SEMS to make data-driven decisions, predict energy demand, and respond to changing conditions in real-time. One of the key applications of ML in SEMS is demand forecasting. ML algorithms analyze vast amounts of historical energy consumption data, weather patterns, and real-time information to predict future energy demand with remarkable accuracy. This capability allows utilities and energy providers to adjust their generation and distribution strategies dynamically, reducing energy wastage and operational costs. Furthermore, ML is instrumental in grid management. Energy grids are complex and vulnerable systems that require constant monitoring and optimization. ML algorithms can process data from sensors and IoT devices placed throughout the grid to detect anomalies, predict potential issues, and recommend preventive measures. This real-time grid management enhances grid stability, reduces the likelihood of outages, and improves overall reliability. In addition to demand forecasting and grid management, ML plays a crucial role in optimizing energy consumption. Residential, commercial, and industrial consumers can leverage ML-powered devices and systems to analyze their energy usag

FAQs

How does machine learning improve smart energy management systems?

Machine learning enables SEMS to make data-driven decisions, predict energy demand, and respond in real time to changing conditions.

What are the main ML applications described in the article?

The article highlights demand forecasting, grid management anomaly detection and preventive recommendations, and optimization of energy use in residential, commercial, and industrial settings.

What challenges can affect ML-powered SEMS performance?

The review emphasizes data quality and integrity as fundamental concerns, and notes scalability challenges as system complexity and deployment increase.

Aug 2, 2026

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