Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-947-2023
https://doi.org/10.5194/wes-8-947-2023
Research article
 | 
07 Jun 2023
Research article |  | 07 Jun 2023

Grand challenges in the digitalisation of wind energy

Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding

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Cited articles

Acumen: Telecom Equipment Market Size, Share, Analysis Report By Component (Hardware, Software), By Infrastructure (Wired, Wireless), By Technology (2G and 3G, 4G LTE, 5G), By End-user (BFSI, Retail, IT and Telecommunication, Media and Entertainment, Healthcare, Military and Defense, Consumer Electronics, Others), And Region Forecast, 2022–2030, https://www.acumenresearchandconsulting.com/telecom-equipment-market, last access: 1 February 2023. a
Ahmed, M. A. and Kim, Y.-C.: Communication network architectures for smart-wind power farms, Energies, 7, 3900–3921, 2014. a
Anderson, M. and Mortensen, N.: Comparative Resource and Energy Yield Assessment Procedures (CREYAP) Pt. II, AWEA Wind Resource & Project Energy Assessment Seminar, 10–11 December 2013, Las Vegas, NV, USA, https://orbit.dtu.dk/en/publications/comparative-resource-and-energy-yield-assessment-procedures (last access: 1 February 2023), 2013. a
Bach-Andersen, M., Winther, O., and Rømer-Odgaard, B.: Scalable systems for early fault detection in wind turbines: a data driven approach, in: Annual Conference of the European Wind Energy Association, 17–20 November 2015, Paris, France, https://www.ewea.org/annual2015/conference/submit-an-abstract/pdf/6401120788396.pdf (last access: 1 February 2023), 2015. a
Benjamin, M., Gagnon, P., Rostamzadeh, N., Pal, C., Bengio, Y., and Shee, A.: Towards Standardization of Data Licenses: The Montreal Data License, arxiv [preprint], https://doi.org/10.48550/ARXIV.1903.12262, 2019.  a
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Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings grand challenges around data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
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