An Optimized Machine Learning Framework for Smart Grid Energy Management

Abstract

The rising need for reliable, efficient and sustainable electricity in modern power systems is stimulating the advancement of smart grid (SG) technologies. Smart grid (SG) technologies are a fast-growing area of the modern power system where their application is increasing due to the need for reliable, efficient and sustainable electricity. Renewable energy resources, distributed energy and advanced communications systems are all interconnected, making the management of energy more complex, and intelligent and optimized decision strategies are required to handle that complexity. This survey seeks to give an overview of optimized machine learning (ML) frameworks for SG energy management. It includes smart grid architecture, energy management systems, ML techniques, optimization methods and their applications, load forecasting, demand response, renewable energy integration, energy consumption optimization, fault detection, and real-time decision making. Furthermore, recent research is discussed that investigates key contributions, current constraints and new trends in research. The survey also considers some of the major challenges, including cybersecurity, scalability, data quality/computational complexity, among others, and outlines future research directions. The results collectively show that the optimized ML frameworks can significantly enhance forecasting accuracy, operational efficiency, grid reliability, and utilization of renewable energy sources, establishing a robust basis for intelligent, resilient, and sustainable smart grid energy management.