Recent Advances in Efficient Energy Management in IoT-Enabled Large Buildings: Giant Trevally Optimizer (GTO) based Electric Vehicle Scheduling, Distributed Resource Integration, and Demand Response Strategies: A Systematic Review

Abstract

The rapid proliferation of Internet of Things (IoT) technologies has significantly transformed energy management in large buildings, enabling real-time monitoring, control, and optimization of complex energy systems. Large-scale infrastructures such as hospitals, campuses, and commercial complexes consume substantial energy, and the integration of distributed energy resources, electric vehicles, and demand response strategies introduces new challenges in managing dynamic and heterogeneous energy loads. This paper presents a comprehensive review of IoT-enabled energy management systems, with a focus on the Giant Trevally Optimizer for electric vehicle charging and discharging scheduling. Inspired by natural hunting strategies, this metaheuristic algorithm effectively addresses multi-objective optimization problems involving cost reduction, peak load minimization, and efficient renewable energy utilization. The review also examines system architectures, including smart metering, cloud-edge computing, and building automation systems, enabling coordinated operation of distributed energy resources. Applications include demand response management, vehicle-to-grid integration, and intelligent load scheduling in smart buildings. Comparative analysis demonstrates that GTO outperforms traditional algorithms such as PSO, GA, and GWO in convergence speed and solution quality. Despite these advantages, challenges related to scalability, interoperability, and cybersecurity persist. This review highlights the potential of combining IoT and advanced optimization techniques to develop efficient, sustainable, and intelligent energy management systems.