• Probabilistic energy forecasting
• Stochastic optimization for energy scheduling problems
• Machine learning and data analysis in the energy domain
• The interface of probabilistic forecasting and stochastic optimization

Valuation of Probabilistic Forecasts in Energy Scheduling Problems

The standard approach for energy scheduling under uncertainty is first to predict these uncertain parameters, such as consumption and prices, and use them in a stochastic optimization. The forecast can be deterministic, univariate probabilistic, or even multivariate probabilistic. Our research shows that not all scheduling problems require multivariate probabilistic forecasts. But while more complex forecast types can offer more information, they might be computationally costly, complex in implementation, and cause sampling errors. Hence, they should only be employed if they lead to better results in the optimization task at hand. Our current research focuses on which type of forecast is sufficient to achieve the best mathematically possible result in a specific energy optimization problem and how specific forecast properties influence optimization results.

EnEff:Stadt Campus Lichtwiese 2 (Subproject: Digital Twin)

The subproject Digital Twin aims to monitor energy flows, predict consumptions and optimize the energy system's operation. To this end, we first implemented a building-level live monitoring system of electricity, heat, and cold consumption. Second, we developed models that use machine learning methods to forecast the building's consumption. Third, we developed a mixed integer linear program that uses these forecasts to compute the optimal operation schedules for controllable consumers and producers.

To support the university's plan to save energy, we applied our research on energy forecasting to quantify a building's weather-independent energy savings. We developed and now maintain an automated tool that allows university employees to assess each building's energy savings on a daily basis.

• Since 2018: PhD student at EINS
• 2015 – 2018: M.Sc. Energy Science and Engineering at TU Darmstadt and École polytechnique fédérale de Lausanne
• 2011– 2015: B.Sc. Physics at TU Darmstadt and University of Bristol