Biography

is an experienced academic professional in Information Technology with over 15 years of teaching and research experience. He holds a Ph.D. in Data Science and specializes in Big Data Analytics, Cloud Computing, and Business Intelligence. He has published over 30 research articles in international journals and conferences. He has supervised several student projects and actively contributes to interdisciplinary research. He has organized seminars and workshops on cloud technologies and analytics platforms. Dr. Aravind is actively involved in curriculum design and academic planning. He collaborates with industry professionals to bring practical insights into the classroom. He is a member of professional organizations and participates in academic review activities. His research interests include predictive analytics and data visualization. He has received recognition for his contributions to teaching and research. He also contributes to book chapters and technical content development. He is dedicated to developing innovative learning environments and mentoring students for academic and professional successdistributed photovoltaics and others, has recently attracted social attention as a means to lessen the impact of pollution, energy crises, and climate change. As the number of distributed energy resources on the load side continues to rise, more and more people are shifting from being traditional power consumers to prosumers, or people that can generate their own electricity[2]. As mentioned, the worldwide trend towards a society with sustainable renewable energy depends on prosumers' active involvement at the end user energy side. Typical prosumers on the grid include commercial buildings with solar PV panels and EV charging stations. The building energy management system can optimize the energy use of these structures under the incentives of electricity pricing. Electricity cost savings, load levelling, and consumption of distributed power are thus promising areas for commercial building energy management. Keywords— Electric Vehicle Charging Stations (EVCSs), Information Technologies (ITs), Operational Technologies (OTs), cyber physical power system (CPPS), knowledge graphs (KGs). I. INTRODUCTION The growth of the EV market has garnered significant interest from governments, automakers, and energy companies. EV are thought to be a practical solution to the growing pollution of the environment and the depletion of fossil fuels. EV charging facilities have proliferated in tandem with the development of EVs. On the other hand, power grid safety and optimal dispatching are threatened by the unpredictable, intermittent, and volatile nature of the load. In order to increase the popularity of EV, rationalize the building of charging stations, and improve prediction precision for optimal dispatching, it is necessary to establish a scientific and realistic short-term load forecasting model for EV charging stations. Focusing studies on load predictions for EV charging stations is thus highly significant[1]. The 979-8-3315-4616-8/26/$31.00 ©2026 IEEE The importance of predicting EV charging demands in power system management has grown in tandem with the booming EV market. While centralized EV charging has many benefits, it also has the potential to significantly increase grid load and strain the power system. An imbalance between the supply and demand for power can cause instability in the system or power outages if these load changes are not correctly predicted. Conversely, efficient energy scheduling and management are two outcomes of reliable demand forecasting that contribute to the grid's capacity to incorporate renewable energy sources[3]. Nevertheless, there are still a lot of obstacles to overcome, even though EV charging load prediction technology are constantly improving[4]. There are notable spatiotemporal features to EV charging loads. While daily, seasonal, and holiday variations affect charging loads in the time dimension, charging demand varies by region in the spatial dimension, with notable distinctions between urban and suburban areas as well as between commercial and non-commercial areas. Large scale EV deployment poses significant hurdles for regulators, power providers, and urban planners despite the fact that it promises a great deal of sustainability[5]. The hardest of them is predicting charging demand accura