Journal Article

Improving Renewable Energy Operations in Smart Grids through Machine Learning

Mishra, Aishwarya, Muralidharan, P., Subramani, K., Pant, Rajesh, Ikhar, Sharayu, Habelalmateen, Mohammed I.

2024

Overview

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.

• Publisher: EDP Sciences
• Subject: Algorithms / Alternative energy sources / Clean energy / Consumption patterns / Decision making / Deep learning / Efficiency / Energy consumption / Energy distribution / Energy efficiency / Energy industry / Energy resources / Energy sources / Green energy / Learning algorithms / Machine learning / Renewable energy / Renewable resources / Simulation / Smart grid / Solar energy / Wind power