I currently work as a senior quantitative analyst with Ørsted in Copenhagen, Denmark. In my role, I focus on two main aspects of trading activities at the front desk. First, I continuously improve probabilistic forecasts to support our traders in selling the wind energy we produce. We are responsible for more than 7.5 GW across multiple European countries, so even small improvements can lead to substantial reductions in balancing costs. Second, I develop algorithms for both asset-backed and non-asset-backed trading strategies.

Before my current position, I worked as a postdoctoral researcher with the Centre for Processes, Renewable Energy Systems PERSEE at MINES Paris, PSL University, in France. My research focused on probabilistic forecasting of renewable energy generation, especially distributed solar power and wind power, in the frame of the European Horizon 2020 project Smart4RES. Besides that, I worked on applications of probabilistic forecasting as well, most notably stochastic optimisation and optimal bidding.

Furthermore, I contributed to Task 16 of the International Energy Agency’s Photovoltaic Power Systems Programme. Task 16 is an international collaboration to define best practises, enhance analysis of long-term inter-annual variability and improve probabilistic and variability forecasting.

Prior to that, I was as a PhD student with the Department of Civil and Industrial Engineering at Uppsala University in Sweden. There, my research focused on spatio-temporal probabilistic forecasting of solar power and electricity demand in the built environment, as well as applications thereof such as stochastic model predictive control. A digital copy of my dissertation is available on DiVA. During my time as a PhD student, I also worked as an external consultant with Greenlytics to investigate the potential of very short-term solar power forecasting using satellite imagery in Sweden.