Expertise
Computer Science
- Reinforcement Learning
- Long Short-Term Memory Network
- Distribution Grid
- Driven Approach
- Newton-Raphson Method
- Learning Approach
Engineering
- Lstm
- Power Flow
Organisations
Publications
2026
Spatio-Temporal Safe Reinforcement Learning for Stochastic Dynamic Optimal Power Flow (2026)[Working paper › Preprint]. ArXiv.org (Submitted). Heydarian Ardakani, A., Hurink, J. L. & Shirazi, E.Actor-Identifier-Critic Reinforcement Learning for Adaptive Model-Free Optimal Control of Nonlinear Systems with Stochastic Packet Dropouts (2026)[Working paper › Preprint]. ArXiv.org. Aqabakee, K., Behnia, K., Ardakani, A. H., Abdollahi, F. & Shirazi, E.https://doi.org/10.48550/arXiv.2605.28569MDN-MPC: Learning EV Charging Behavior with Mixture Density Networks for Controlling PV Charging Stations (2026)IEEE Transactions on Transportation Electrification, 12(2), 3525-3535. Ardakani, A. H., Nijenhuis, B., Hurink, J., Gibson, I. & Shirazi, E.https://doi.org/10.1109/TTE.2026.3656021
2025
Attention-Based Temporal Reinforcement Learning for Energy System Control (2025)In 2025 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT Europe 2025 (IEEE PES Innovative Smart Grid Technologies Conference Europe). IEEE. Heydarian Ardakani, A., Hurink, J., Gibson, I. & Shirazi, E.https://doi.org/10.1109/ISGTEurope64741.2025.11305619Multi-temporal PV power prediction using long short-term memory and wavelet packet decomposition (2025)Energy and AI, 21. Article 100540. Sardarabadi, A., Heydarian Ardakani, A., Matrone, S., Ogliari, E. & Shirazi, E.https://doi.org/10.1016/j.egyai.2025.100540Probabilistic Forecast of EV Charging Demand using Quantile Regression and LSTM with Attention Mechanism (2025)In E-ENERGY '25: Proceedings of the 2025 16th ACM International Conference on Future and Sustainable Energy Systems (pp. 1005-1007) (E-ENERGY Conference Proceedings; Vol. 2025). Association for Computing Machinery, Inc. Matrone, S., Heydarian Ardakani, A., Ogliari, E., Shirazi, E. & Leva, S.https://doi.org/10.1145/3679240.3734687Embedding Temporal Awareness in Reinforcement Learning Models for Energy System Control (2025)In E-ENERGY 2025 - Proceedings of the 2025 16th ACM International Conference on Future and Sustainable Energy Systems (pp. 1002-1004). Association for Computing Machinery. Ardakani, A. H., Hurink, J., Gibson, I. & Shirazi, E.https://doi.org/10.1145/3679240.3734686Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution grids (2025)[Working paper › Preprint]. Yan, S., Vazinram, F., Kaseb, Z., Spoor, L., Stiasny, J., Mamudi, B., Heydarian Ardakani, A., Orji, U., Vergara, P. P., Xiang, Y. & Guo, J.https://doi.org/10.48550/arXiv.2504.11650Scalable Charging Optimization of Battery Energy Storage Systems with Deep Reinforcement Learning (2025)In IEEE PES Innovative Smart Grid Technologies Europe, ISGT EUROPE 2024. IEEE. Ardakani, A. H., Aqabakee, K., Abdollahi, F. & Shirazi, E.https://doi.org/10.1109/ISGTEUROPE62998.2024.10863295Data Driven Approach towards More Efficient Newton-Raphson Power Flow Calculation for Distribution Grids (2025)[Contribution to conference › Abstract] ICT With Industry 2025. Guo, J., Heydarian Ardakani, A., Kaseb, Z., Mamudi, B., Spoor, L., Vazinram, F., Orji, U., Stiasny, J., Vergara, P. P., Xiang, T. & Yan, S.
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