Improving Renewable Energy Operations in Smart Grids through Machine Learning

^* Assistant Professor, School of Business and Management, Christ university yeshwanthpur campus Bangalore .
^† Assistant Professor, School of Business and Management, CHRIST(Deemed to be University ) Bangalore Yeshwantpur Campus
^‡ The Islamic university, Najaf, Iraq
^§ Uttaranchal Institute of Management, Uttaranchal University Uttarakhand, India
^** Department of Computer Science & Engineering, IES College of Technology, IES University, Madhya Pradesh 462044 India, Bhopal .
⁶ Researcher, Yashika Journal Publications Pvt Ltd, India Email: sharyu.ikhar@gmail.com, Wardha, Maharashtra

^* Corresponding Author : muralidharan.p@christuniversity.in
^† Subramani.k@christuniversity.in
^‡ mohammed.ha@iunajaf.edu.iq
^§ rajeshpant.mech@gmail.com
^** research@iesbpl.ac.in

Abstract

This paper reviews the work in the areas of machine learning’s role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future.