A Comprehensive AI-Based Digital Twin Model for Residential Hydrogen-Based Energy Systems

As the urgency to mitigate climate change intensifies, the residential sector, a significant contributor to greenhouse gas emissions, calls for innovative solutions to foster decarbonization efforts. The integration of renewable energy sources and hydrogen-based technologies offers a promising pathway to achieve energy independence and so reduce reliance on traditional power grids. In this sense, digital twins, powered by artificial intelligence techniques, offer significant potential to enhance the performance of these systems, fostering energy self-sufficiency. This paper presents a comprehensive architecture for a digital twin of residential hydrogen-based energy systems. We discuss the implementation of the digital replica based on both logical behavior and machine learning techniques. The resulting models are validated using real data collected from an electrically self-sufficient social housing in Spain, located in the town of Novales (Cantabria). The results evince that the behavior of the proposed solution accurately mimics the one shown by the physical counterpart, suggesting its utility as a valuable instrument for enhancing the efficiency of renewable hydrogen-based energy systems.