AI Model-Blockchain Integrated Decentralized Smart Grid Framework as a Pedagogical Model for Future Engineering Technology Education

Ilham Husam Hasan Albakri; Haider TH.Salim ALRikabi

doi:10.3991/ijep.v16i4.61699

Authors

Ilham Husam Hasan Albakri Wasit University, Wasit, Iraq
Haider TH.Salim ALRikabi Wasit University, Wasit, Iraq https://orcid.org/0000-0002-5201-2084

DOI:

https://doi.org/10.3991/ijep.v16i4.61699

Keywords:

Smart Grid, Deep Learning, GRU, Blockchain, Energy Trading, Dynamic Pricing, Engineering Education, Industry 4.0

Abstract

Industry 4.0 is a term that depends on many underlying technologies. One of the most important technologies is these key technologies that are aimed at changing the current energy infrastructure. Digital key technologies, like blockchain, smart grids, and artificial intelligence (AI), are among the technologies that provide the base of transformation towards Industry 4.0. Smart grids provide real-time monitoring and integration of distributed renewable energy sources. AI will assist in the analysis of large data sets regarding energy consumption. Blockchain provides secure and decentralized networks that mechanically facilitate financial transactions via smart contracts and enable transparent trading of energy. However, with the advancements in technology, engineering education will increasingly be demanded to give students interdisciplinary skills reflecting the integration of many elements within modern energy systems. Traditional methods of teaching usually treat the above technologies as separate disciplines when, in Industry 4.0 environments, students need to have an understanding of how the different cutting-edge technologies fit together within real-world energy system management situations for there to be interdisciplinary learning opportunities. This paper aims to provide engineering students with an opportunity to gain hands-on experience that bridges between theoretical academic knowledge and practical application of key technologies within Industry 4.0. To facilitate the objective, the paper suggests an integrated AI–Blockchain smart grid framework that integrates blockchain-enabled decentralized energy trading with deep learning (DL)-based electrical demand forecasts to close this gap. The framework is considered a practical use case that comes with the theoretical and implementation aspects to allow engineering students to gain overall knowledge. The framework incorporates three layers: the first layer is the data source, which collects operational data from the smart grid environment. The second layer, the analysis, is an analysis of data using DL algorithms that forecast future electricity aspects. The last layer, the decentralized control, regulates and manages decentralized electricity trading by calculating the dynamic price of electricity using smart contracts on the blockchain network. Framework architecture and implementation details and components are comprehensively discussed to feed the literature with the required aspects of key technologies’ integration. Finally, the paper discusses expected outcomes and future direction and opens issues towards continuing the research in this important area and provides students with associated interdisciplinary skills.

Author Biography

Haider TH.Salim ALRikabi, Wasit University, Wasit, Iraq

Wasit University,College of Engineering,Electrical Engineering Department

References

[1] U. Islam, H. Ullah, N. Khan, K. Saleem, and I. Ahmad, “AI-enhanced intrusion detection in smart renewable energy grids: A novel industry 4.0 cyber threat management approach,” International Journal of Critical Infrastructure Protection, vol. 50, Sep. 2025, doi: 10.1016/j.ijcip.2025.100769.

[2] M. Faheem, H. Kuusniemi, B. Eltahawy, M. S. Bhutta, and B. Raza, “A lightweight smart contracts framework for blockchain-based secure communication in smart grid applications,” IET Generation, Transmission and Distribution, vol. 18, no. 3, pp. 625–638, Feb. 2024, doi: 10.1049/gtd2.13103.

[3] R. Tuanaya, R. Madyasta, A. Wibowo, F. Amruzain, S. Wibowo, and G. K. Kassymova, “Understanding Education 4.0 through a Systematic Review of Pedagogical Innovations, Technology Integration, and Systemic Challenges,” Journal of Technological Pedagogy and Educational Development, vol. 104, no. 3, pp. 104–118, 2025, doi: 10.59247/jtped.v2i3.25.

[4] A. M. Al Shareef, S. Seçkiner, B. Eid, and H. Abumeteir, “Integration of blockchain with artificial intelligence technologies in the energy sector: a systematic review,” 2024, Frontiers Media SA. doi: 10.3389/fenrg.2024.1377950.

[5] B. Zafar and S. Ben Slama, “Energy Internet Opportunities in Distributed Peer-to-Peer Energy Trading Reveal by Blockchain for Future Smart Grid 2.0,” Nov. 01, 2022, MDPI. doi: 10.3390/s22218397.

[6] K. Su, Y. Yu, and J. Zhang, “Blockchain-based smart grid power trading technology,” Journal of Engineering and Applied Science, vol. 71, no. 1, Dec. 2024, doi: 10.1186/s44147-024-00562-z.

[7] K. Singh, S. B. Goyal, A. S. Rajawat, and H. N. Waked, “A Blockchain-Integrated AI Framework for Enhancing Energy Efficiency and Sustainability in Smart Grids,” in Procedia Computer Science, Elsevier B.V., 2025, pp. 2302–2311. doi: 10.1016/j.procs.2025.04.485.

[8] Y. Y. Ghadi et al., “A hybrid AI-Blockchain security framework for smart grids,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-05257-w.

[9] H. Sun and T. Weingärtner, “A Blockchain-Based Architecture for Energy Trading to Enhance Power Grid Stability,” Electronics (Switzerland), vol. 14, no. 23, Dec. 2025, doi: 10.3390/electronics14234629.

[10] K. Su, Y. Yu, and J. Zhang, “Blockchain-based smart grid power trading technology,” Journal of Engineering and Applied Science, vol. 71, no. 1, Dec. 2024, doi: 10.1186/s44147-024-00562-z.

[11] I. U. Khan, N. Javeid, C. J. Taylor, K. A. A. Gamage, and X. Ma, “A Stacked Machine and Deep Learning-Based Approach for Analysing Electricity Theft in Smart Grids,” IEEE Trans. Smart Grid, vol. 13, no. 2, pp. 1633–1644, Mar. 2022, doi: 10.1109/TSG.2021.3134018.

[12] X. Fang, S. Misra, G. Xue, and D. Yang, “Smart grid - The new and improved power grid: A survey,” 2012, Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/SURV.2011.101911.00087.

[13] H. Gharavi and R. Ghafurian, “Smart grid: The electric energy system of the future,” in Proceedings of the IEEE, Institute of Electrical and Electronics Engineers Inc., 2011, pp. 917–921. doi: 10.1109/JPROC.2011.2124210.

[14] S. Tiwari, “Artificial Intelligence (AI) in the Sustainable Energy Sector †,” Engineering Proceedings, vol. 37, no. 1, 2023, doi: 10.3390/ECP2023-14609.

[15] W. Hua, Y. Chen, M. Qadrdan, J. Jiang, H. Sun, and J. Wu, “Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review,” Jun. 01, 2022, Elsevier Ltd. doi: 10.1016/j.rser.2022.112308.

[16] P. Kumar, R. Kumar, A. Aljuhani, D. Javeed, A. Jolfaei, and A. K. M. N. Islam, “Digital twin-driven SDN for smart grid: A deep learning integrated blockchain for cybersecurity,” Solar Energy, vol. 263, Oct. 2023, doi: 10.1016/j.solener.2023.111921.

[17] J. L. P. Calle, L. Ferrari, A. Ruiz-Rodríguez, M. Ferreiro-González, and M. Palma, “Application of FT-IR and machine learning in the characterization of organic and biodynamic wines vs conventional wines,” Applied Food Research, vol. 6, no. 1, p. 101898, Jun. 2026, doi: 10.1016/j.afres.2026.101898.

[18] H. Bhatt, S. Rana, and S. S. Dubey, “Blockchain-driven decentralized data and access control services for smart grid,” Cluster Comput., vol. 28, no. 12, Nov. 2025, doi: 10.1007/s10586-025-05347-4.

[19] K. Singh, S. B. Goyal, A. S. Rajawat, and H. N. Waked, “A Blockchain-Integrated AI Framework for Enhancing Energy Efficiency and Sustainability in Smart Grids,” in Procedia Computer Science, Elsevier B.V., 2025, pp. 2302–2311. doi: 10.1016/j.procs.2025.04.485.

[20] C. Yapa, C. De Alwis, M. Liyanage, and J. Ekanayake, “Utilization of a blockchainized reputation management service for performance enhancement of Smart Grid 2.0 applications,” J. Ind. Inf. Integr., vol. 39, May 2024, doi: 10.1016/j.jii.2024.100580.

[21] P. Balakumar, R. S. Kumar, R. Saravanakumar, and S. Saravanan, “Optimizing smart grid performance: A GRU approach to electric power forecasting and demand side management,” Results in Engineering, vol. 29, Mar. 2026, doi: 10.1016/j.rineng.2026.109868.

[22] Y. Wan, T. Kober, and M. Densing, “Nonlinear inverse demand curves in electricity market modeling,” Energy Econ., vol. 107, Mar. 2022, doi: 10.1016/j.eneco.2022.105809.

[23] M. M. Khan, F. S. Khan, M. Nadeem, T. H. Khan, S. Haider, and D. Daas, “Scalability and Efficiency Analysis of Hyperledger Fabric and Private Ethereum in Smart Contract Execution,” Computers, vol. 14, no. 4, Apr. 2025, doi: 10.3390/computers14040132.

[24] A. D. Lopes, T. Mello, and W. dos R. Bezerra, “Digital identity management system with blockchain:An implementation with Ethereum and Ganache,” Jul. 2025, [Online]. Available: http://arxiv.org/abs/2507.21398

[25] G. Iuliano, D. Carangelo, C. Calabrese, and D. Di Nucci, “MuSe: a Mutation Testing Plugin for the Remix IDE,” Mar. 2026, doi: 10.1109/SANER67736.2026.00098.

[26] G. Korpal and D. Scott, “Decentralization and web3 technologies,” May 12, 2022. doi: 10.36227/techrxiv.19727734.v1.