TY - GEN A1 - Klein, Marten A1 - Medina Méndez, Juan Alí A1 - Schöps, Mark Simon A1 - Schmidt, Heiko A1 - Glawe, Christoph T1 - Towards physics-based nowcasting of the instantaneous wind velocity profile using a stochastic modeling approach T2 - STAB Jahresbericht 2024 zum 24. DGLR-Fachsymposium der STAB, 13. - 14. November 2024, Regensburg N2 - The primary objective of this contribution is to provide an overview of the regime-spanning forward modeling capabilities offered by the stochastic one-dimensional turbulence model. The focus is on the applicability of the model and its validation for neutral and stable atmospheric boundary layer flows as a prerequisite for future applications to challenging atmospheric conditions. KW - atmospheric boundary layer KW - stochastic modeling KW - one-dimensional turbulence KW - wind energy KW - wind shear and veer Y1 - 2024 UR - https://bfm.rcbe.de/files/2024/11/2024_STAB-Jahresbericht.pdf SP - 162 EP - 163 PB - Deutsche Strömungsmechanische Arbeitsgemeinschaft, STAB CY - Regensburg [et al.] ER - TY - GEN A1 - Klein, Marten A1 - Medina Méndez, Juan Alí A1 - Schmidt, Heiko T1 - Simulating Volatile Wind Energy: Stochastic Forward Modeling and Machine Learning N2 - The transformation of the energy sector is based on the integration of various renewable sources, such as wind and solar energy. One of the key challenges for the integration of these sources into the existing power grid is their erratic and sometimes discontinuous availability (volatility). Wind energy is one of the most relevant sources of CO2 neutral electric energy, but volatile due to fluctuating wind fields on multiple scales. This has already been realized so that senors provide real-time information on the scale of individual wind turbines. However, fore- casting remains an unresolved problem since numerical weather prediction models cannot provide the necessary level of detail. New modeling strategies are required that integrate turbine-scale and meso-scale information for accurate site-specific short-term prediction. Present and forthcoming research aims to incorporate fluctuations on multiple levels of fidelity, depending on the abstraction layer KW - AI transfer KW - AI teaching KW - stochastic modeling KW - machine learning KW - wind energy KW - volatility modeling and prediction Y1 - 2022 UR - https://ai-science-atlas.innohub13.de/files/pdf/NSGSimulatingVolatileWindEnergyBTUpdf.pdf UR - https://www-docs.b-tu.de/fg-stroemungsmodellierung/public/Klein_poster_KI-Atlas22.pdf PB - Innovation Hub 13, TH Wildau CY - Wildau ER -