Efficient Energy Management in IoT-Enabled Buildings Using Giant Trevally Optimizer for Electric Vehicle Scheduling

Abstract

The rapid adoption of Internet of Things (IoT) technologies has significantly transformed energy management in large buildings, enabling real-time monitoring and intelligent control of complex energy systems. These buildings account for a substantial share of global energy consumption, and the integration of renewable energy sources, energy storage systems, and electric vehicles introduces new challenges in optimizing energy usage under dynamic and uncertain conditions.

This paper presents a comprehensive review of IoT-enabled building energy management systems, with particular emphasis on the Giant Trevally Optimizer for electric vehicle charging and discharging scheduling. Inspired by biological hunting strategies, this metaheuristic algorithm effectively addresses multi-objective optimization problems, including cost minimization, peak load reduction, and enhanced utilization of renewable energy. The review further explores system architectures such as smart metering, building automation systems, and cloud-edge computing frameworks for efficient coordination of distributed energy resources.

Applications include demand response strategies, vehicle-to-grid integration, and intelligent load scheduling in smart buildings. Comparative analyses indicate that metaheuristic approaches outperform traditional optimization techniques in managing nonlinear and stochastic energy systems. However, challenges related to interoperability, cybersecurity, and scalability persist. This review highlights the potential of integrating IoT with advanced optimization methods to develop sustainable and intelligent building energy management solutions.