Evolving Trends in Energy Management Systems (EMS) Research Using Scientometric Analysis

Energy Management Systems (EMS) play a vital role in optimizing energy usage, reducing environmental impact, and enhancing energy efficiency in both microgrid and residential applications. The increasing complexity of energy systems, coupled with advancements in artificial intelligence (AI) and demand response mechanisms, has led to rapid evolution in EMS research. However, this progress has resulted in fragmented knowledge, making it difficult to discern overarching trends and identify key contributions in the field. This study addresses the following questions: What are the major trends, influential works, and future directions in EMS research over the past five decades? To answer this, a comprehensive scientometric analysis of 3,709 publications (1973–2023) was conducted using the Web of Science database and analyzed through CiteSpace. The analysis reveals significant shifts in EMS research, transitioning from traditional microgrid-focused studies to broader applications in smart homes, AI-driven load scheduling, and advanced optimization techniques. Key findings highlight the growing emphasis on integrating AI, machine learning, and real-time decision-making to enhance the responsiveness and sustainability of EMS. Additionally, the study identifies critical contributions from influential publications that provide a roadmap for developing next-generation EMS frameworks. These works emphasize the importance of addressing emerging challenges, such as the need for adaptive load management, enhanced grid stability, and increased reliance on renewable energy sources. By synthesizing historical trends and future directions, this study offers valuable insights for researchers, practitioners, and policymakers to advance innovations in energy management, contributing to a more sustainable and efficient energy landscape.