Secure hydrogen production analysis and prediction based on blockchain service framework for intelligent power management system

Harun Jamil, Faiza Qayyum, Naeem Iqbal, Murad Ali Khan, Syed Shehryar Ali Naqvi, Salabat Khan, Do Hyeun Kim

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
63 Downloads (Pure)

Abstract

The rapid adoption of hydrogen as an eco-friendly energy source has necessitated the development of intelligent power management systems capable of efficiently utilizing hydrogen resources. However, guaranteeing the security and integrity of hydrogen-related data has become a significant challenge. This paper proposes a pioneering approach to ensure secure hydrogen data analysis by integrating blockchain technology, enhancing trust, transparency, and privacy in handling hydrogen-related information. Combining blockchain with intelligent power management systems makes the efficient utilization of hydrogen resources feasible. Using smart contracts and distributed ledger technology facilitates secure data analysis (SDA), real-time monitoring, prediction, and optimization of hydrogen-based power systems. The effectiveness and performance of the proposed approach are demonstrated through comprehensive case studies and simulations. Notably, our prediction models, including ABiLSTM, ALSTM, and ARNN, consistently delivered high accuracy with MAE values of approximately 0.154, 0.151, and 0.151, respectively, enhancing the security and efficiency of hydrogen consumption forecasts. The blockchain-based solution offers enhanced security, integrity, and privacy for hydrogen data analysis, thus advancing clean and sustainable energy systems. Additionally, the research identifies existing challenges and outlines future directions for further enhancing the proposed system. This study adds to the growing body of research on blockchain applications in the energy sector, specifically on secure hydrogen data analysis and intelligent power management systems.
Original languageEnglish
Pages (from-to)3192-3224
Number of pages33
JournalSmart Cities
Volume6
Issue number6
Early online date22 Nov 2023
DOIs
Publication statusPublished - Dec 2023

Keywords

  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Urban Studies

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