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| Funder | European Commission |
|---|---|
| Recipient Organization | Universidad de Zaragoza |
| Country | Spain |
| Start Date | Sep 01, 2024 |
| End Date | Aug 31, 2028 |
| Duration | 1,460 days |
| Number of Grantees | 18 |
| Roles | Associated Partner; Coordinator; Participant |
| Data Source | European Commission |
| Grant ID | 101168673 |
Training Wind Energy Experts on Digitalisation (TWEED) Doctoral Network (DN) aims to train the next generation of excellent researchers equipped with a full set of technical and complementary skills to develop high-impact careers in wind energy digitalization.
This goal will be achieved through an outstanding research-for-innovation programme, and a unique training programme that combines hands-on research training, interactive schools and hackathons, innovation management and placements with non-academic partner organizations An integrated research program will be carried out alongside the development of a Virtual Wind Energy Data Science Hub that facilitates a virtual research environment to foster collaboration, data sharing and testing of innovative solutions seeking drastic cost-of-energy reductions.
The network will provide an interdisciplinary and inter-sectoral context to foster creativity in transforming technological challenges into data science problems that can be solved to develop prototypes for commercial exploitation.
Researchers will be trained in business innovation to extend their focus beyond the academic context to be able to identify added-value products or services through customer discovery and guidance from established researchers and entrepreneurs.
As a result, a research-for-innovation mindset will be developed to provide enhanced career perspectives to the fellows, equipped with a complete set of thematic, technological and innovation skills.
Moreover, TWEED network will accomplish the foundation of Wind Energy Data Science (WEDS) as a new research discipline across the EAWE network to raise awareness, exploit synergies, and motivate future career choices.
The potential of data-intensive science as a new paradigm will allow reducing the cost of energy by as much as 13% during the project lifetime and 50% by 2050 by intelligent solutions not accessible to traditional wind energy specialists.
Excellence in Renewables Private Company - Iwind Renewables Pc; Universidad de Zaragoza; Ethnicon Metsovion Polytechnion; Technische Universitaet Muenchen; Enbw Energie Baden-Wurttemberg Ag; Annea Ai Unipessoal Lda; Compañía Eólica de Tierras Altas S.A.; Rtdt Laboratories Ag; Eidgenoessische Technische Hochschule Zuerich; Stichting Maritiem Research Instituut Nederland; Danmarks Tekniske Universitet; Belgisch Laboratorium Van Elektriciteitsindustrie; European Academy of Wind Energy; Edf Energy R&D Uk Centre Limited; Ost - Ostschweizer Fachhochschule; Norges Teknisk-Naturvitenskapelige Universitet Ntnu; Technische Universiteit Delft; Dnv Services Uk Limited
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