1 citations · 2 across the 8 of their papers we have counts for
10 papers
Decision-Aware Training for Sample-Based Generative Models
Kornelius Raeth, Nicole Ludwig
Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's…
Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks
Kornelius Raeth, Nicole Ludwig
Standard weather forecast evaluations focus on the forecaster's perspective and on a statistical assessment comparing forecasts and observations. In practice, however, forecasts ar…
Bounded Graph Clustering with Graph Neural Networks
Kibidi Neocosmos, Diego Baptista, Nicole Ludwig
In community detection, many methods require the user to specify the number of clusters in advance since an exhaustive search over all possible values is computationally infeasible…
Fault Detection in Solar Thermal Systems using Probabilistic Reconstructions
Florian Ebmeier, Nicole Ludwig, Jannik Thuemmel +2
Solar thermal systems (STS) present a promising avenue for low-carbon heat generation, with a well-running system providing heat at minimal cost and carbon emissions. However, STS…
Assessing the risk of future Dunkelflaute events for Germany using generative deep learning
Felix Strnad, Jonathan Schmidt, Fabian Mockert +2
The European electricity power grid is transitioning towards renewable energy sources, characterized by an increasing share of off- and onshore wind and solar power. However, the w…
RainShift: A Benchmark for Precipitation Downscaling Across Geographies
Paula Harder, Luca Schmidt, Francis Pelletier +5
Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolution for local-scale risk-assessments is…
A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation
Jonathan Schmidt, Luca Schmidt, Felix Strnad +2
Local climate information is crucial for impact assessment and decision-making, yet coarse global climate simulations cannot capture small-scale phenomena. Current statistical down…
Climate data selection for multi-decadal wind power forecasts
Sofia Morelli, Nina Effenberger, Luca Schmidt +1
Reliable wind speed data is crucial for applications such as estimating local (future) wind power. Global Climate Models (GCMs) and Regional Climate Models (RCMs) provide forecasts…
Turbine location-aware multi-decadal wind power predictions for Germany using CMIP6
Nina Effenberger, Nicole Ludwig
Climate change will impact wind and therefore wind power generation with largely unknown effect and magnitude. Climate models can provide insights and should be used for long-term…
Wind Power Assessment based on Super-Resolution and Downscaling -- A Comparison of Deep Learning Methods
Luca Schmidt, Nicole Ludwig
The efficient placement of wind turbines relies on accurate local wind speed forecasts. Climate projections provide valuable insight into long-term wind speed conditions, yet their…