Optimizing Energy Efficiency in Smart Grids Using Machine Learning Algorithms: A Case Study in Electrical Engineering

Abstract

Today's electrical grids are growing exponentially more complex and 21st century trends, such as the addition of renewable energy sources and increased demand elasticity, challenge traditional approaches to promoting greater efficiencies in how we consume power. This is where traditional management techniques seem to fail and results in more energy losses & therefore resources being spent on operations. The increasing penetration of renewable sources with its inherent variability, machine learning (ML), as a data-driven intelligent approach is considered to be able to control the grid. In conclusion, this paper addresses how ML techniques such as regression models, decision trees and neural networks could increase prediction accuracy, balance distribution of loads classes and improve stability for smart grids. Findings from the study show that along hospitality customers' backgrounds, machine learning algorithms can provide energy consumption predictions with better accuracy than neural networks (over 95% and for both a reduction up to 20 % on total losses in energy), coupled with additional stability requirements during peak hours which would demand an allocation between15%. Utility and consumer savings from these advances equated to 16% ROI in year-one costs by third-party renewable developers, providing a significant cost reduction These results imply that ML has the potential to revolutionize how energy management is handled in smart grids, offering a swift and cheap resolution to issues surrounding modern power system.

Basically, ML has great potential in resolving grid management issues such as demand prediction, load balancing and system stability. Yet the challenges of quality data, transparency in model explanation and security stand between the promise on this new scale; additional research is needed here.