Artificial Intelligence Techniques for Efficient Energy Management in IoT-Enabled Large Buildings: Giant Trevally Optimizer (GTO) based Electric Vehicle Scheduling, Distributed Resource Integration, and Demand Response Strategies: Trends and Challenges

Abstract

The rapid proliferation of Internet of Things (IoT) technologies has transformed energy management in large buildings, enabling real-time monitoring, automation, and data-driven optimization. As buildings account for a significant share of global energy consumption, the integration of electric vehicles, distributed energy resources, and smart grid interactions introduces complex challenges requiring intelligent and adaptive management solutions. This paper presents a comprehensive review of artificial intelligence-driven energy management frameworks, with a focus on the Giant Trevally Optimizer (GTO). As a bio-inspired metaheuristic algorithm, GTO effectively addresses multi-objective optimization problems in building energy systems, including cost minimization, peak load reduction, and renewable energy utilization. The review also examines the integration of deep learning techniques, IoT-based sensing, and predictive models to enhance decision-making in electric vehicle scheduling, demand response strategies, and distributed energy resource coordination.

Applications include smart charging of electric vehicles, microgrid optimization, and intelligent load management in IoT-enabled buildings. Comparative analysis demonstrates that GTO-based and hybrid AI approaches outperform traditional optimization techniques in scalability, convergence speed, and efficiency. However, challenges such as interoperability, cybersecurity, and real-time deployment remain. This review highlights the potential of combining IoT, AI, and advanced optimization methods to develop sustainable, scalable, and intelligent energy management systems for future smart cities.