LITERATURE REVIEW: ADVANCES IN SMART LOAD MANAGEMENT SYSTEMS FOR ENERGY EFFICIENCY AND DEMAND RESPONSE

Abstract

The optimization of energy management in smart grids is crucial for enhancing energy efficiency, ensuring grid stability, and promoting the integration of renewable energy sources. In recent years, the adoption of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has paved the way for smarter, more efficient energy management solutions. These technologies enable real-time data collection, predictive modeling, and dynamic decision-making for optimal energy distribution, especially in demand-side management (DSM) and load forecasting. This paper explores the role of these technologies in smart grid systems, emphasizing their contribution to improving energy efficiency in smart homes, buildings, and industrial processes. Furthermore, it investigates the integration of electric vehicles (EVs) and renewable energy sources like solar and wind in smart grid operations. The challenges and opportunities associated with data security, privacy, and system scalability are also discussed. By analyzing recent advancements and future trends, this paper presents a comprehensive review of the state-of-the-art in energy management for smart grids. The findings demonstrate that, despite the challenges, IoT, AI, and ML are key enablers in creating sustainable, efficient, and resilient energy systems.