PhD student, Polytech Annecy-Chambéry.
----------Contact----------
Email Zaid.Allal@univ-smb.fr
Gmail : dr.allal.zaid@gmail.com
LinkedIn->https://www.linkedin.com/in/allal-zaid-14b400255/
Phone Phone: +33 (0)4 50 09 xx xx
Wtsp: +212 (0)658640961
Office : Axxx
Address LISTIC, 5 chemin de bellevue, CS80439, 74 944 Annecy Cedex, France
----------Thesis----------
Theme : ReGaRD
Subject Efficient and robust machine learning solutions for renewable energy and hydrogen systems.
Summary:
The transition to sustainable and renewable energy sources, and the use of hydrogen as an energy carrier, has become a focal point of global energy policies. Machine learning (ML) has the potential to address these challenges and revolutionize the way we harness and use renewable energy and hydrogen.This thesis presents a comprehensive study to investigate and develop machine learning applications in the field of renewable energy systems. This study will focus on three use cases of ML/DL models for different hydrogen/renewable energy processes, namely: (a) production, (b) storage and (c) distribution, as well as the possible scenario of combining hydrogen energy with renewable energy systems. The results of this thesis will contribute to the advancement of smart, automated, sustainable and clean modern energy solutions, capable of reducing carbon emissions and mitigating the impacts of climate change.
Keywords :
- Data pre- and post-processing
- Predictive maintenance and fault detection
- Machine/deep learning
- Reinforcement learning
- Hydrogen-based energy systems
- Renewable energy systems
Publications HAL
- Predicting Power Consumption Using Machine Learning Techniques->https://hal.science/hal-04698565v1
- Wind turbine fault detection and identification using a two-tier machine learning framework-> https://hal.science/hal-04564635v1
Publications Other
- Efficient health indicators for the prediction of the remaining useful life of proton exchange membrane fuel cells-> https://www.sciencedirect.com/science/article/pii/S2590174523001599
- Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions -> https://www.sciencedirect.com/science/article/abs/pii/S0301479724003785
- Leveraging the power of machine learning and data balancing techniques to evaluate stability in smart grids->https://www.sciencedirect.com/science/article/abs/pii/S0952197624004627
- Machine Learning Algorithms for Solar Irradiance Prediction: A Recent Comparative Study->https://www.sciencedirect.com/science/article/pii/S2772671124000354.
- A comparative study of ensemble methods and multi-output classifiers for predictive maintenance of hydraulic systems->https://www.sciencedirect.com/science/article/pii/S2590123024011551
----------Thesis Direction ----------
Co-supervisor Flavien Vernier
Supervisor Hassan Noura
Start of thesis :12-10-2023
Doctoral school SISEO "Sciences et Ingénierie des Systèmes, de l'Environnement et des Organisations" (Science and Engineering of Systems, the Environment and Organizations)