Work

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

Year: 2025

Type: article

Abstract: 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.

Source: IEEE Open Journal of the Computer Society

DOI: 10.1109/ojcs.2025.3594439

Authors: Laura Rodríguez de Lope, Victor M. Maestre, Luis Diez, Alfredo Ortiz, Ramón Agüero, Inmaculada Ortiz

Institution: Universidad de Cantabria

Language: English

Topic: Hybrid Renewable Energy Systems

Subfield: Energy Engineering and Power Technology

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,075