Open Access
| Issue |
EPJ Web Conf.
Volume 328, 2025
First International Conference on Engineering and Technology for a Sustainable Future (ICETSF-2025)
|
|
|---|---|---|
| Article Number | 01015 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/epjconf/202532801015 | |
| Published online | 18 June 2025 | |
- Xiao, P., Yang, S., Zhang, Z. et al. Edge-assisted and energy-efficient access control for dynamic users group in smart grids. Peer-to-Peer Netw. Appl. 17, 1149–1157 (2024). https://doi.org/10.1007/s12083-024-01655-5 [Google Scholar]
- Ethirajan, V., Mangaiyarkarasi, S.P. An in-depth survey of latest progress in smart grids: paving the way for a sustainable future through renewable energy resources. Journal of Electrical Systems and Inf Technol 12, 9 (2025). https://doi.org/10.1186/s43067-025-00195-z [CrossRef] [Google Scholar]
- Al Essa, M.J.M. A review on price-driven energy management systems and demand response programs in smart grids. Environ Syst Decis 45, 7 (2025). https://doi.org/10.1007/s10669-024-09998-3 [CrossRef] [Google Scholar]
- Abassi, A., El Jai, M., Arid, A. et al. Modeling and Mitigating Billing Attacks in Scalable Smart Grids with Distributed and Intelligent Systems. Oper. Res. Forum 6, 17 (2025). https://doi.org/10.1007/s43069-025-00414-3 [CrossRef] [Google Scholar]
- Yuanjian, Z., Tianci, Z., Zhengjun, J. et al. A blockchain-based privacy-preserving data aggregation scheme with robustness in smart grids. J Supercomput 81, 675 (2025). https://doi.org/10.1007/s11227-025-07156-3 [CrossRef] [Google Scholar]
- Nasir, Q., Abu Talib, M., Arshad, M.A. et al. Comparison of deep learning algorithms for site detection of false data injection attacks in smart grids. Energy Inform 7, 71 (2024). https://doi.org/10.1186/s42162-024-00381-9 [CrossRef] [Google Scholar]
- Safari, A., Gharehbagh, H.K., Nazari-Heris, M., Zare, K. (2024). Design of a Dynamic Feedback LSTM Electricity Price Forecast of Smart Grids. In: Azad, S., Nazari-Heris, M. (eds) Artificial Intelligence in the Operation and Control of Digitalized Power Systems. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-69358814 [Google Scholar]
- Harb, H., Hijazi, M., Brahmia, MEA. et al. An intelligent mechanism for energy consumption scheduling in smart buildings. Cluster Comput 27, 11149–11165 (2024). https://doi.org/10.1007/s10586-024-04440-4 [CrossRef] [Google Scholar]
- Gurjar, G., Nikose, M.D. Smart Contract Framework for Secure and Efficient P2P Energy Trading with Blockchain. J. Electr. Eng. Technol. 20, 255–269 (2025). https://doi.org/10.1007/s42835-024-02043-y [Google Scholar]
- Manzoor, A., Judge, M.A., Islam, S.u. et al. AHHO: Arithmetic Harris Hawks Optimization algorithm for demand side management in smart grids. Discov Internet Things 3, 3 (2023). https://doi.org/10.1007/s43926-023-00028-3 [CrossRef] [Google Scholar]
- Naji El Idrissi, R., Ouassaid, M. & Maaroufi, M. Game Theory Approach for Energy Consumption Scheduling of a Community of Smart Grids. J. Electr. Eng. Technol. 18, 2695–2708 (2023). https://doi.org/10.1007/s42835-023-01379-1 [Google Scholar]
- Ramya, R.R., Banumathi, J. An optimized approach with 128-bit key management for IoT-enabled smart grid: enhancing efficiency, security, and sustainability. Electr Eng 107, 2207–2225 (2025). https://doi.org/10.1007/s00202-024-02636-w [CrossRef] [Google Scholar]
- Triet, N.M. et al. (2025). Blockchain-Enhanced Energy Trading in Smart Cities and Grids: Advancements in Market Systems and Business Models. In: Jorgensen, B.N., Ma, Z.G., Wijaya, F.D., Irnawan, R., Sarjiya, S. (eds) Energy Informatics. EI.A 2024. Lecture Notes in Computer Science, vol 15272. Springer, Cham. https://doi.org/10.1007/978-3-031-74741-06 [Google Scholar]
- Mishra, K., Basu, S. & Maulik, U. A classification framework for demand side management in residential smart grids. Int J Data Sci Anal (2025). https://doi.org/10.1007/s41060-025-00735-w [Google Scholar]
- Najibi, S., Najafi, M., Mallaki, M. et al. Resilience enhancement program in smart grids by coordinating demand response and optimal reconfiguration during wildfires. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02749-2 [Google Scholar]
- Ravinder, M., Kulkarni, V. Smart Grid Anomaly Detection Using MFDA and Dilated GRU-based Neural Networks. Smart Grids and Energy 10, 9 (2025). https://doi.org/10.1007/s40866-024-00216-2 [CrossRef] [Google Scholar]
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