Digital Twin framework for a Smart Hydrogen Energy District: Real-time monitoring and probabilistic predictive analytics

We report on the design and implementation of a unified Digital Twin (DT) framework for a Smart Hydrogen Energy District (SHED). The framework is designed to overcome the limitations of high-frequency data streams which can create cognitive overload for operators. This SHED-DT enables real-time monitoring, immersive user-interface interactions, virtual exploration, and probabilistic predictive analytics for the integrated energy system. The system integrates sensor data from multiple sources and it is an interactive platform enables operators to navigate the integrated energy system while monitoring live performance metrics. The backend synchronizes and cleans over a thousand signals with a mean latency of 2.5 s and greater than 99.9% completeness, while the frontend renders a live, navigable 3D DT of the district. The DT’s functionality includes probabilistic time-series models that process both historical and real-time data to predict energy production and demand. The integration of probabilistic forecasting model forecasting provides reliability over traditional deterministic methods. The forecasting model achieves a mean continuous ranked probability score (CRPS) of 0.13, which reduces in forecast error by more than 50% relative to a deterministic baseline, and an ablation study shows that the immersive 3D interface reduces operator reaction time by approximately 40% compared to conventional 2D dashboards. In combination with high-fidelity immersive visualization, the DT supports operators in real-time decision making and energy management of district clean energy systems. The platform is demonstrated with a case study that highlights its role in improving efficiency, reliability, and sustainable energy visualization and planning. Preliminary findings demonstrate that the platform improves situational awareness and reducing operator reaction times. This work bridging the gap between physical infrastructure and cyber–physical decision support systems.