• Description:

    Short-term energy forecasting for the next minutes to days ahead, is a prerequisite for the economic and safe operation of modern power systems and electricity markets especially under high renewable energy sources (RES) penetration. The different contexts of application make that end-users require models that have a broad number of properties especially when they are applied operationally. They should cover multiple time frames (from minutes to days ahead) and multiple RES technologies (i.e. wind, solar, hydro) as well as their aggregations (i.e. in the form of virtual power plants – VPP). They should use as input the very large amount of data available, while dealing efficiently with dimensionality. The data sources may be measurements from the power plants, various types of satellite images, sky camera images, various feeds of numerical weather prediction and others. They should be generic enough to be easily replicable to different sites or demand forecasting. They should also be resilient against imperfect or corrupted data streams; be interpretable enough; and be able to deal with structural changes in the physical system (e.g. addition of assets to a VPP or equipment in a smart home). So far separate models are developed for each of these aspects. The thesis is realized in the frame of the PEPR TASE project Fine4Cast coordinated by the supervisors of this thesis. PERSEE has an international visibility in the field of energy forecasting thanks to a long track of national and European projects, PhDs and publications in the area.

    Qualifications 

    Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained).

    Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg R, Python, Julia,…).  A succesful candidate will have a solid background in three or more of the following competencies:

     

     

  • Fields

    • Computer Science

    • Data Science

  • Qualifications

    • Master

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