The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21939995

Singh, Deepali (2023): Training and validation datasets for training probabilistic machine learning models on NREL's 10-MW reference wind turbine. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21939995.v1

Dataset

• Interdisciplinary Engineering
• Renewable Energy

conditional distribution, machine learning, OpenFAST, probabilistic surrogate, stochastic data, surrogate modeling

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Deepali Singh

This repository consists of two databases- CASE-ONSHORE and CASE-OFFSHORE, generated using OpenFAST v2.4 on NREL's 10-MW reference wind turbine for training data-driven probabilistic load surrogate models. The data is to be used for mapping 10-minute average environmental conditions to the corresponding 10-minute load statistics such as load average, fatigue and range at various locations on the tower and blades.

• 2023-01-25 first online, published, posted

4TU.ResearchData

dat files

Probabilistic Surrogate Modeling of Site-Specific Loads on Onshore and Offshore Wind Turbines Using Mixture Density Networks

• https://github.com/IEAWindTask37/IEA-10.0-198-RWT

• Novel deSign, producTion and opEration aPproaches for floating WIND turbine farms (grant code 860737) [more info...] European Commission

TU Delft, Faculty of Aerospace Engineering, Department of Flow Physics and Technology