A Survey on the Influence of Blockchain and Neural Network Technologies on Smart Grid Systems
DOI:
https://doi.org/10.70411/MJHAS.2.2.2025210Keywords:
Blockchain, Neural Network Technologies, Smart Grids, Security and Reliability, Applications of Blockchain and Neural Network TechnologiesAbstract
Energy transactions benefit from blockchain technology because it creates an independent system that provides secure data protocols and protects against unauthorised alterations of information while strengthening protection. Multipurpose blockchain tools let traders conduct secure peer-to-peer energy exchanges, and they can run automated networks while diminishing dependence on central control systems. Small and distributed energy control methods decrease infrastructure vulnerability by shielding it from cyber threats while creating a tamperproof network, which builds modern energy security and visibility. Organisations use advanced machine learning algorithms to study historical and real-time data to optimise energy distribution, identify consumption patterns, and detect anomalies. The predictive models enable better decision-making through preventive maintenance plans, reduced energy losses, and optimised distribution of electricity loads in the grid system. The research investigates detailed implementations of predictive models that unite neural networks and blockchain technology into a system that builds up smart grid functions. These technologies form a synergistic relationship that enables smart automation systems along with continuous real-time monitoring and boosted operational efficiency. The study investigates actual blockchain-based energy market examples alongside case studies on predictive maintenance carried out through AI and decentralised power trading platforms. The research results show that integrating blockchain with neural networks can enhance smart grid security and reliability while also supporting the development of a sustainable and adaptable energy system. Through their resolution of scalability problems and regulatory requirements as well as computational limits, these technologies establish future frameworks for smart power networks of the next generation. The survey findings create a basis for researchers to use for advancing energy informatics and smart grid innovation in the future.
References
Abdellatif, & et al. (2025). Blockchain-enabled distributed learning for enhanced smart grid security and efficiency. Computers and Electrical Engineering, 110012.
Ahmed, T., & et al. (2022). Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 112128.
Ali, A., & et al. (2023). Artificial intelligence application in demand response: advantages, issues, status, and challenges. IEEE access, 16907-16922.
Almalaq, A., & et al. (2017). A review of deep learning methods applied on load forecasting. IEEE international conference on machine learning and applications (ICMLA), 511-516.
Almasoudi, F. (2023). Enhancing power grid resilience through real-time fault detection and remediation using advanced hybrid machine learning models. Sustainability, 8348.
Alotaibi, I., & et al. (2020). A comprehensive review of recent advances in smart grids: A sustainable future with renewable energy resources. Energies, 6269.
Anjimoon, S., & et al. (2024). Secure and Sustainable Energy Distribution through Blockchain Technology in Smart Grids. E3S Web of Conferences, 02002.
Belchior, & et al. (2021). A survey on blockchain interoperability: Past, present, and future trends. Acm Computing Surveys (CSUR), 1-41.
Ferdous, M., & et al. (2023). Leveraging self-sovereign identity & distributed ledger technology in renewable energy certificate ecosystems. Journal of Cleaner Production, 138355.
Gil-González, W., & et al. (2020). Optimal placement and sizing of wind generators in AC grids considering reactive power capability and wind speed curves. Sustainability, 2983.
Gonen, T. (2014). Electric power distribution engineering. Taylor & Francis Group.
Gürbüz, k. (2019). Smart grid applications and technologies in distribution systems. Master's thesis, Middle East Technical University (Turkey).
Hafid, A., & et al. (2020). Scaling blockchains: A comprehensive survey. IEEE access, 125244-125262.
Haghnegahdar, L., & et al. (2020). A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection. Neural computing and applications, 9427-9441.
Hua, W., & et al. (2022). Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review. Renewable and Sustainable Energy Reviews, 112308.
Islam, M., & et al. (2024). Improving reliability and stability of the power systems: A comprehensive review on the role of energy storage systems to enhance flexibility. IEEE Access.
Javaid, N., & et al. (2018). Energy efficient integration of renewable energy sources in the smart grid for demand side management. IEEE access, 77077-77096.
Khalid, M. (2024). Smart grids and renewable energy systems: Perspectives and grid integration challenges. Energy Strategy Reviews, 101299.
Khan, A., & et al. (2023). Artificial intelligence and blockchain technology for secure smart grid and power distribution Automation: A State-of-the-Art Review. Sustainable Energy Technologies and Assessments, 103282.
Kumar, & et al. (2023). Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cybersecurity. Solar Energy, 111921.
Li, j., & et al. (2023). Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management. Energy and AI, 100208.
Musleh, A., & et al. (2019). Blockchain applications in smart grid–review and frameworks. Ieee Access, 86746-86757.
Marimi, R. M. (2025). Study the impact of integrating blockchain and neural network technologies on predictive models: A survey. International science and technology journal, 36(1), 1–25. http://www.doi.org/10.62341/rmms1757.
Prakash, R., & et al. (2022). Blockchain technology for cybersecurity: A text mining literature analysis. International Journal of Information Management Data Insights, 100112.
Ressi, & et al. (2024). AI-enhanced blockchain technology: A review of advancements and opportunities. Journal of Network and Computer Applications, 103858.
Sanka, & et al. (2021). A systematic review of blockchain scalability: Issues, solutions, analysis and future research. Journal of Network and Computer Applications, 103232.
Singh, U., & et al. (2021). A machine learning-based gradient boosting regression approach for wind power production forecasting: A step towards smart grid environments. Energies, 5196.
Treiblmaier, H. (2023). A comprehensive research framework for Bitcoin’s energy use: Fundamentals, economic rationale, and a pinch of thermodynamics. Blockchain: Research and Applications, 100149.
Waseem, M., & et al. (2023). Incorporation of blockchain technology for different smart grid applications: Architecture, prospects, and challenges, Energies, 820.
Waseem, M., & et al. (2023). Incorporation of blockchain technology for different smart grid applications: Architecture, prospects, and challenges. Energies, 820.
Wu, Y., & et al. (2022). Towards collective energy Community: Potential roles of microgrid and blockchain to go beyond P2P energy trading, Applied Energy, 119003.
Wu, Y., & et al. (2022). Towards collective energy Community: Potential roles of microgrid and blockchain to go beyond P2P energy trading. Applied Energy, 119003.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Modern Journal of Health and Applied Sciences
This work is licensed under a Creative Commons Attribution 4.0 International License.
The users are free to:
- Share — copy and redistribute the material in any medium or format.
- Adapt — remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
