A Novel Sustainable Approach for Site Selection of Underground Hydrogen Storage in Poland Using Deep Learning

Reza Derakhshani (), Leszek Lankof, Amin GhasemiNejad, Alireza Zarasvandi, Mohammad Mahdi Amani Zarin and Mojtaba Zaresefat ()
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Reza Derakhshani: Department of Earth Sciences, Utrecht University, 3584 CB Utrecht, The Netherlands
Leszek Lankof: Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, Wybickiego 7A, 31-261 Krakow, Poland
Amin GhasemiNejad: Department of Economics, Faculty of Management and Economics, Shahid Bahonar University of Kerman, Kerman 7616913439, Iran
Alireza Zarasvandi: Department of Geology, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz 6135743136, Iran
Mohammad Mahdi Amani Zarin: Department of Computer Sciences, Shahid Bahonar University of Kerman, Kerman 7616913439, Iran
Mojtaba Zaresefat: Copernicus Institute of Sustainable Development, Utrecht University, 3584 CB Utrecht, The Netherlands

Energies, 2024, vol. 17, issue 15, 1-13

Abstract: This research investigates the potential of using bedded salt formations for underground hydrogen storage. We present a novel artificial intelligence framework that employs spatial data analysis and multi-criteria decision-making to pinpoint the most appropriate sites for hydrogen storage in salt caverns. This methodology incorporates a comprehensive platform enhanced by a deep learning algorithm, specifically a convolutional neural network (CNN), to generate suitability maps for rock salt deposits for hydrogen storage. The efficacy of the CNN algorithm was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), and the Correlation Coefficient (R 2 ), with comparisons made to a real-world dataset. The CNN model showed outstanding performance, with an R 2 of 0.96, MSE of 1.97, MAE of 1.003, and RMSE of 1.4. This novel approach leverages advanced deep learning techniques to offer a unique framework for assessing the viability of underground hydrogen storage. It presents a significant advancement in the field, offering valuable insights for a wide range of stakeholders and facilitating the identification of ideal sites for hydrogen storage facilities, thereby supporting informed decision-making and sustainable energy infrastructure development.

Keywords: underground hydrogen storage; deep learning; site selection; convolutional neural networks; sustainable energy storage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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