A Survey of Methods and Architectures for Efficient Energy Management in IoT-Enabled Large Buildings: Giant Trevally Optimizer (GTO) based Electric Vehicle Scheduling, Distributed Resource Integration, and Demand Response Strategies

Abstract

The rapid integration of Internet of Things (IoT) technologies into large-scale buildings has significantly transformed building energy management systems by enabling real-time monitoring, control, and optimization. As buildings account for a major share of global energy consumption and emissions, the increasing integration of electric vehicles, distributed energy resources, and demand response mechanisms introduces complex, dynamic, and multi-objective optimization challenges that require advanced intelligent solutions. This paper presents a comprehensive review of IoT-enabled energy management frameworks, with a focus on the Giant Trevally Optimizer (GTO) for optimizing electric vehicle scheduling, distributed energy resource coordination, and demand response strategies. Inspired by natural predatory behavior, GTO provides an effective balance between exploration and exploitation, enabling efficient handling of high-dimensional and nonlinear optimization problems. The review also examines system architectures, including IoT sensing networks, edge-cloud computing, and machine learning-based forecasting for real-time decision-making. Applications include vehicle-to-grid integration, renewable energy management, and intelligent load scheduling in smart buildings. Comparative studies show that GTO-based and hybrid AI frameworks outperform traditional optimization methods in cost reduction, peak demand management, and renewable utilization. However, challenges such as scalability, cybersecurity, interoperability, and real-world deployment remain. This review highlights the potential of integrating IoT, AI, and advanced optimization techniques to develop efficient, scalable, and sustainable energy management systems for future smart buildings.