Data Science for Energy Engineers
This course provides a broad introduction to the many use cases of data science in the energy domain, with an emphasis on energy flexibility and demand side management.
Praktische info:
Inschrijven?
- Inschrijvingen: tot 20 mei 2022
- Prijs: EIT InnoEnergy Master School studenten: 0€; Andere: 1000€ (excl. VAT).
georganiseerd door:
Taking a hands-on approach, this course leads you through the entire data science pipeline for concrete energy use cases.
Over the course, you will learn how to analyse, visualize, forecast and optimize energy demand using a number of different tools in Python. You will also gain hands-on knowledge about practical tools of the trade for sharing analysis results with other stakeholders via dashboards and tracking results of your own experiments using state of the art tools.
Energy data science gives you the tools to:
- Be able to ask better questions about energy data (and answer them),
- Understand the industrial context in which these data science algorithms are applied,
- Possess practical skills to load, explore, analyse and visualize various energy datasets,
- Be able to make energy (demand) forecasts using machine learning models, while also understanding their limitations and how they build on time series and statistical principles,
- Know how to optimize the behaviour of energy flexible resources given arbitrary cost functions (ranging from minimizing costs to grid peaks and carbon emissions),
- Be able to track your experiments using state-of-the-art tools,
- Be able to present the results of your analysis in a manner accessible to both specialists and non-specialists.
Now in its fourth iteration, the course has been specifically designed based on industrial requirements, and feedback from hundreds of learners in collaboration with experts from leading European universities including KU Leuven, KTH, UPC and Grenoble INP.
Energy data science gives you the tools to:
- Be able to ask better questions about energy data (and answer them),
- Understand the industrial context in which these data science algorithms are applied,
- Possess practical skills to load, explore, analyse and visualize various energy datasets,
- Be able to make energy (demand) forecasts using machine learning models, while also understanding their limitations and how they build on time series and statistical principles,
- Know how to optimize the behaviour of energy flexible resources given arbitrary cost functions (ranging from minimizing costs to grid peaks and carbon emissions),
- Be able to track your experiments using state-of-the-art tools,
- Be able to present the results of your analysis in a manner accessible to both specialists and non-specialists.
Lesgevers / sprekers
Hussain Kazmi is currently an FWO postdoctoral research fellow at KU Leuven, where his research is at the intersection of machine learning, optimal decision making and energy. His core area of expertise lies in developing algorithms that integrate domain expertise and downstream task information into data-driven models for smart(er) energy systems. Very recently, this research work has received the annual award of the International Institute of Forecasters. Currently, he leads a cross-European EIT InnoEnergy working group aimed at developing a data science program for energy engineers.
He holds a PhD at the intersection of data science and energy engineering from KU Leuven (2019), as well as MSc degrees in Sustainable Energy Technology, and Energy and Nuclear Engineering from Technical University of Eindhoven (The Netherlands) and Politecnico di Torino (Italy) respectively. In 2021, he was a visiting research scholar at KTH Royal Institute of Technology (Sweden). As the first data scientist at two different Belgian clean energy startups (Enervalis and iLECO), he has also helped set up data science teams in applied settings.
Application of ICT systems for reliable and cost efficient operation of power systems. Especially the architecture and characteristics of the ICT solutions given changing market requirements.
Management of research projects and ensuring good integration between university research and industry needs.
Associate Professor at KTH Royal Institute of Technology. Research focus: control, optimization, stability and cybersecurity of microgrids and power electronics based power systems.
