- Najibi, F., Apostolopoulou, D. and Alonso, E. (2021). Enhanced performance Gaussian process regression for probabilistic short-term solar output forecast. International Journal of Electrical Power and Energy Systems, 130. doi:10.1016/j.ijepes.2021.106916.
- Najibi, F., Apostolopoulou, D. and Alonso, E. (2021). Enhanced Performance Gaussian Process Regression for Probabilistic Short-term Solar Output Forecast. International Journal of Electrical Power and Energy Systems, 130(106916). doi:10.1016/j.ijepes.2021.106916.
- Najibi, F., Niknam, T. and Kavousi-Fard, A. (2016). Optimal stochastic management of renewable MG (micro-grids) considering electro-thermal model of PV (photovoltaic). Energy, 97, pp. 444–459. doi:10.1016/j.energy.2015.12.122.
- Najibi, F. and Niknam, T. (2015). Stochastic scheduling of renewable micro-grids considering photovoltaic source uncertainties. Energy Conversion and Management, 98, pp. 484–499. doi:10.1016/j.enconman.2015.03.037.
London EC1V 0HB
Fatemeh is a PhD. Candidate at the City University of London under the supervision of Eduardo Alonso and Dimitra Apostolopoulou.
She works on multidisciplinary projects such as renewable energy predictive models, Microgrid optimization, Optimal Transmission-Distribution power system coordination.
Before joining the PhD, she involved in both academia and industry. She worked as a researcher for Iran Power Transmission, Generation and Distribution Company (TAVANIR) from 2015-2018 and has experience with different industrial software such as CYMDIST, DIgSILENT, ArcGIS, AutoCAD, NEPLAN, DIALux as well as programming languages such as MATLAB, Python, R and GAMS.
Fatemeh has a master’s degree in the electrical engineering power system. During her Master, she was working on Probabilistic Microgrid optimization, and she published two journal papers.
She works as a GTA in the Department of Computer Science and Electrical Engineering at the City University of London. The course she is helping with are: Mathematic in computing, Operating System, System Architecture, introduction to algorithms, Power System Design and Engineering Mathematics.
• Power systems dynamics and operation
• Deep learning
• Dynamic Modeling of Renewable Energy Sources
• Smart Grids
• Machine learning
• Optimization methods
• Electric vehicles
• Stochastic programming
• Intelligent Optimization Algorithms
• Probabilistic analysis and correlation studies
• Solar energy
• Mathematical and evolutionary computations
PROFESSIONAL ACTIVITIES & AWARDS
• Reviewer of UKACC Control Conference since 2018.
• Reviewer of International Journal of Energy and Power Engineering since 2019.
• Reviewer of ICREN renewable energy Conference since 2019.
• Won London 2050 Mega-City essay Competition, 2019.
• Teaching associate fellow certificate, The Higher Education Academy, 2018, UK.
• MATLAB &Simulink certificate, Technical and Vocational Training Corporation, Shiraz, Iran, Oct. 2010.
• Research Scholarship Award of National Elite Institute, Shiraz University of Technology, Iran.
• Award as a promotion for the best consultant for Iran Power Transmission, Generation and Distribution
Company (TAVANIR), Shiraz, Iran, 2015.
• Development and innovation, Technical and Vocational Training Corporation, Shiraz, Iran, Oct. 2010.
• Poster presentation in PhD symposium city university of London, July 2018.
1. F. Najibi, T. Niknam, A. Kavousi-fard, “Optimal stochastic management of renewable MG (micro-grids) considering the electro-thermal model of PV (photovoltaic)”, Energy. 2016. Cited by 18.
2. F. Najibi, T. Niknam, “Stochastic scheduling of renewable micro-grids considering photovoltaic source uncertainties”, Energy Conversion and Management. 2015. Cited by 49.
3. F. Najibi, E. Alonso, D. Apostolopoulou, “Optimal Dispatch of Pumped Storage Hydro Cascade Under Uncertainty”, UKACC 12th International Conference on Control (CONTROL), 2018, Sheffield, UK, 5-7 September 2018
1. Fatemeh Najibi, CYMDIST guide, a booklet for Iran Power Generation and Transmission Company (TAVANIR), in Persian, 2015.
2. Fatemeh Najibi, DigSilent guide, a booklet for Iran Power Generation and Transmission Company (TAVANIR), in Persian, 2015.
- Deep learning in probabilistic optimal Micro Grid operation, City University of London, London, United Kingdom, Jan 2018
- Research PhD student, City University of London, Jan 2018 – present
Title of thesis: Machine Learning for Power System Optimization
Jan 2018 – Mar 2021
Summary of research
My research is mainly about using Deep learning and optimization tools to solve an optimal probabilistic microgrid operation problem. I predict renewable energy resources output using machine learning and deep learning tools.
Publications by category
Conference papers and proceedings (2)
- Najibi, F., Apostolopoulou, D. and Alonso, E. (2021). Clustering Sensitivity Analysis for Gaussian Process Regression Based Solar Output Forecast. IEEE PowerTech Conference 27 Jun 2021 – 2 Jul 2021, Madrid, Spain.
- Najibi, F., Alonso, E. and Apostolopoulou, D. (2018). Optimal Dispatch of Pumped Storage Hydro Cascade under Uncertainty.