@article{10902/37114,
year = {2025},
month = {8},
url = {https://hdl.handle.net/10902/37114},
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 article 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.},
organization = {This work was supported in part by the Spanish Government (Ministry of Science and Innovation) and the European Union (Next GenerationEU/RTRP) through the Projects “Digital twin of a hybrid solar photovoltaic-hydro hybrid system for residential supply” under Grant TED2021-129951B-C22, in part by the “Demonstration pilot of a solar-photovoltaic-hydrogen hybrid system for residential energy supply” under Grant TED2021-129951B-C21, in part by (Ministry Economic Affairs and Digital Transformation) “SITED: Semantically-enabled Interoperable Trustworthy Enriched Data-spaces” under Grant PID2021-125725OB-I00, and in part by the Government of Cantabria through the Project “Enabling Technologies for Digital Twins and their application in the chemical and communications sectors” (GDQuiC) of the TCNIC Program under Grant 2023/TCN/002.},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
publisher = {IEEE Open Journal of the Computer Society, 2025, 6, 1317-1328},
title = {A comprehensive aI-based digital twin model for residential hydrogen-based energy systems},
author = {Rodríguez de Lope López, Laura and Maestre Muñoz, Víctor Manuel and Díez Fernández, Luis Francisco and Ortiz Sainz de Aja, Alfredo and Agüero Calvo, Ramón and Ortiz Uribe, Inmaculada},
}