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

Abstract

The increasing demand for power driven by the integration of renewable energy sources has created an urgent need to improve energy efficiency in smart grids Conventional power grids with unidirectional power supply and midstream industries struggling to accommodate variable renewable energy and increased demand consumption. In response, this research explores the use of machine learning (ML) algorithms as a solution to increase energy efficiency, and presents a data-driven approach to address the challenges of modern smart grids meeting the solution for. The effectiveness of the algorithms is examined. Specifically, the research aims to (1) investigate how ML models can improve power delivery and reduce power consumption, (2) differ in key metrics such as accuracy and responsiveness, among others and regression, clustering, and neural networks -Time for performance testing ML algorithms, and (3) ML applications in smart networks Address practical challenges, such as data quality and computational requirements. To achieve these objectives, the study seeks to provide actionable insights for practitioners and researchers aiming to adopt ML solutions for sustainable energy use. The results of this study show that the ML algorithm significantly increases the energy consumption of smart grids. Through predictive modeling and optimization, the ML model achieved a 15% improvement in energy efficiency, a 25% reduction in peak demand, and an annual cost savings of approximately $200,000 Furthermore, predicting a ML-driven maintenance enabled early detection of potential grid issues, reducing downtime , technical losses and it has been reduced. These findings highlight the potential of ML to address today’s complex energy systems, delivering robust, scalable, and efficient solutions that support the integration and powering of renewable energy sources sustainable planning goals are advanced.