Neural network based approach to design complex PV systems

Genk - PhD
|
More than two weeks ago

to develop and implement data-driven approaches for modelling and optimizing the long-term outdoor performance of complex PV systems

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Currently, over 80% of the world's primary energy supply is provided by fossil fuel sources (coal, oil, gas) causing significant greenhouse gas emissions and contributing to the constant warming of our planet. In order to limit climate change and provide a sustainable answer to our growing electricity demand, a green energy transition needs to happen as quickly as possible. Electricity generation using solar panels has a very important role to play in decarbonizing the electricity sector, while providing further advantages such as electricity access in remote locations and developing countries.

Past research on photovoltaic technology brought improvements of solar cell efficiency, which now approach theoretical limits and also helped to reduce the installation costs of photovoltaic power plants, making this green technology cost competitive with traditional electricity sources. One of the main remaining challenges is the accurate prediction and optimization of the outdoor performance of photovoltaic power plants over their entire lifetime, which is a crucial element in unlocking the full potential of this green energy source.

IMEC and EnergyVille – as world-leading semiconductor and energy research institutes – have a track record in PV outdoor performance research, encompassing both outdoor monitoring and physics-based energy yield modelling activities. As both physics-based modelling and outdoor monitoring become time and resource-intensive processes for large systems, we are looking for new routes to develop accurate and scalable methods.

The goal of the present PhD project is to develop and implement data-driven approaches for modelling and optimizing the long-term outdoor performance of complex PV systems, including the effects of ageing mechanisms. The project is strongly supported by in-house expertise on modelling, optimizing and monitoring PV systems, as well as technology insight into ageing processes.

We are looking for a highly motivated candidate, who is interested in acquiring a deep knowledge of the theory and modelling of PV electricity generation; the theory and application of data-driven modelling methods and willing to actively contribute to the green energy transition. The PhD student will work in one of the world's leading semi-conductor and energy research centers in a group of young scientists and engineers supporting this thesis.

Required background: 

  • Master's in engineering or computer science
  • Very good English language skills
  • Strong empathy for theory, modelling and data analytics


Type of work: 15% literature study + 60% modeling + 25% data analytics 

Required background: the theory and application of data-driven A person interested in modelling methods and willing to actively contribute to the green energy transition

Supervisor: Francky Catthoor

Daily advisor: Imre T Horvath

The reference code for this position is 2020-073. Mention this reference code on your application form.
Chinese nationals who wish to apply for the CSC scholarship, should use the following code when applying for this topic: CSC2020-35.

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