Deep-S2SWind: A data-driven approach for improving Sub-seasonal wind predictions (Proposals Track)

Noelia Otero Felipe (University of Bern); Pascal Horton (University of Bern)

Paper PDF Slides PDF Recorded Talk NeurIPS 2022 Poster Topia Link Cite
Climate Science & Modeling Earth Observation & Monitoring Interpretable ML Time-series Analysis


A major transformation to mitigate climate change implies a rapid decarbonisation of the energy system and thus, increasing the use of renewable energy sources, such as wind power. However, renewable resources are strongly dependent on local and large-scale weather conditions, which might be influenced by climate change. Weather-related risk assessments are essential for the energy sector, in particular, for power system management decisions, for which forecasts of climatic conditions from several weeks to months (i.e. sub-seasonal scales) are of key importance. Here, we propose a data-driven approach to predict wind speed at longer lead-times that can benefit the energy sector. The main goal of this study is to assess the potential of machine learning algorithms to predict periods of low wind speed conditions that have a strong impact on the energy sector.

Recorded Talk (direct link)