A Comprehensive Review of LightConneuNet: Potential Analysis of Fuel Cell Vehicle-To-Grid System with Large-Scale Buildings

Abstract

The transition toward sustainable energy systems has accelerated the integration of hydrogen fuel cell vehicles, vehicle-to-grid (V2G) technologies, and intelligent building energy management systems. Large buildings are emerging as critical nodes in multi-directional energy networks, where efficient coordination of renewable resources, storage systems, and flexible loads is essential. Fuel cell vehicles, offering zero-emission energy generation, present new opportunities for supporting grid stability and optimizing building energy consumption. This paper presents a comprehensive review of LightConneuNet, a lightweight deep learning architecture designed for efficient energy forecasting and optimization in fuel cell V2G-enabled building systems. By combining depthwise separable convolutions, dense connectivity, and attention mechanisms, LightConneuNet achieves high predictive accuracy with reduced computational complexity, making it suitable for real-time and edge-based applications. The review evaluates its performance against conventional deep learning models in forecasting energy demand, managing hydrogen storage, and optimizing bidirectional power flow. Applications include smart energy scheduling, peak load reduction, hydrogen energy management, and V2G integration across large building infrastructures. Empirical findings demonstrate significant improvements in forecasting accuracy, energy cost reduction, and system efficiency. However, challenges such as hydrogen infrastructure limitations, system integration, and regulatory constraints remain. This review highlights the potential of combining deep learning and hydrogen-based energy systems to develop efficient, scalable, and sustainable building energy management solutions.