Report
Overview of wind energy digitalisation
Overview of wind energy digitalisation. Photo credit: Sarah Barber, OST

Task 43 on Digitalisation in Wind Energy

Annual Report 2025

Task 43

Authors: Shawn Sheng, National Laboratory of the Rockies (NLR), USA; Sarah Barber, Eastern Switzerland University of Applied Sciences, Switzerland

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Objectives

Digitalisation is one of the key barriers to increasing the value of wind energy. A recent Task 43 publication [1] defined the "grand challenges" to digitalisation in wind energy to be: (1) Data: creating FAIR (findable, accessible, interoperable, and reusable) data frameworks; (2) Culture: connecting people and data to foster innovation; (3) Coopetition : enabling collaboration and competition between organisations. These grand challenges include a mix of technical, cultural, and business aspects that will need collaboration across industry, academia, and government to solve. This is the focus of the current (second) phase of IEA Wind Task 43, which finishes in October 2026. The main objectives of Task 43 in this second phase are:

  • Learn about data, digital twins, knowledge graphs and knowledge engineering (defined in [2]); in particular, how these can increase data FAIRness and increase the value of wind energy.
  • Publish recommendations for improving data FAIRness and for creating and publishing ontologies in the sector.
  • Publish recommendations for classifying and valuing digital twins in the sector.
  • Develop and publish existing and new ontologies collaboratively, in order to improve the interoperability of wind energy data.
  • Publish a data maturity roadmap to help the sector plan collaborative activities.
  • Understand and demonstrate how organisational culture and coopetition between organisations can be improved in the sector to foster digitalisation, and publish guidelines for doing so.
  • Carry out "deep dive" use cases and develop best practices for efficient data usage in real applications.

Participation

The countries currently participating in Task 43 are listed in Table 1. Observing countries include China, France, Poland, South Korea, and Spain. Theree are currently 194 members and observers.

Table 1. Participants
No. Country/Sponsor Institution(s)
1SwitzerlandEastern Switzerland University of Applied Sciences, ETH Zurich, University of Basel, Microsoft, HES-SO
2USANLR, EPRI, Georgia Tech, Apex Clean Energy, Wood, DNV, UL, Shell, NIST, BSEE, University of Miami, Ocergy, Nextera, BP, AWS, Ramboll, Altosphere, Ocergy, PNNL, RWE, Sandia
3IrelandServusNet, Brightwind, University College Dublin, Atlantic Technological University Sligo, Microsoft, Green Rebel, IMR, South East Technological University
4SwedenRISE, Luleå University of Technology, SR Energy AB, Fortum
5CanadaUniversity of Windsor, UL Solutions, Southern Alberta Institute of Technology, University of Victoria, Agriculture and Agri-Food Canada (AAFC), WEICAN, Zero Nexu
6DenmarkDTU Wind, Aalborg University, SEWPG European Innovation Center ApS, Twind Solutions, R&D Test Systems, Orsted
7NetherlandsTU Delft, Twindo, Suzlon, Wageningen University, TouchWind b.v., Whiffle, Leiden University, Microsoft, Vattenfall
8UKOctue, Natural Power, Bitbloom, Carbon Trust, BayWa r.e., Ramboll, The Crown Estate, Oldbaum Services, University College London, SSE Renewables, RES, Durham University, UKRI, Vatenfall, ARMSA Academy, E&P Consulting, EPAN Systems, Interface Insight, JNBP, KUDO Software, Loughborough University, Marine AI, Nuveen Infrastructure, PowerVeritas, Shell, Teraom, Univeristy of Northumbria, University of Strathclyde
9GermanyFraunhofer IWES, enerlace, Ramboll, University Stuttgart, Turbit Systems, University of Oldenburg, Technical University Munich, Badenova, Dhara Consulting Services, DLR, Fugro, Ramboll Deutschland

Progress, Results, and Impact in 2025

In 2025, we continued our regular webinar series with three webinars [3]. In the area of "Data", based on our review paper "Knowledge engineering for wind energy" [2], we created the "Recommended Practices on Creating and Publishing Ontologies for Wind Energy", submitted to the ExCo in February 2026 (Figure 1) [4]. A related recommendation, "Best Practice Recommendations for Improving FAIR Data Maturity in Wind Energy", was published by members of IEA Wind Task 43 under the auspices of the Research Data Alliance [5]. We continued to work on new ontologies, including a new Wind Energy Ontology and Operations Ontology [6]. We extended and improved the Digital Twins Taxonomy [7] and used it as a basis for a new "Joint Best Practice on Classifying and Valuing Digital Twins in Wind Energy", submitted to the ExCo in March 2026 together with the European Academy of Wind Energy [8]. The TechnoPortal is being maintained and used for publishing ontologies, now hosting 28 ontologies [9]. We published the IEA Wind TCP Technical Report "Evolving the wind energy sector towards frictionless and sustainable data usage" [10].

Overview of the recommendations from the Recommended Practices on Creating and Publishing Ontologies for Wind Energy
Figure 1. Overview of the recommendations from the "Recommended Practices on Creating and Publishing Ontologies for Wind Energy" [4]. Source: IEA Wind Task 43

We submitted the results of the IEA Wind Task 43 Culture Questionnaire 2024 as a paper called "Understanding the effect of organisational culture on digitalisation" [11]. Based on this, we published an IEA Wind TCP Technical Report "A guide to improving digital organisational culture in wind energy" in February 2026 [12]. A IEA Wind TCP Technical Report "Skills mapping for Wind Resource Assessment" is about to be published.

We launched a new WeDoWind platform in 2025, expanding the original idea of bringing together data owners with researchers through data science "challenges" (such as how to improve the accuracy of fault detection methods) to a more holistic ecosystem [13]. We ran four new challenges and undertook a feasibility study and submitted the results to the Wind Energy Science Journal in a paper titled "Fostering open science through a digital open innovation platform – structural health monitoring case study" in July 2025 [14]. We currently have more than 500 users worldwide and growing.

The Use Case coordination team developed a "Value Demonstration Checklist" and a "Value Demonstration Template", which will be used in the future to evaluate our value demonstration activities. As well as a new release of Wind Resource Assessment data model by including floating LIDAR file format [15], we developed a risk-based Bayesian decision-making framework for leading edge erosion and presented a methodology for optimised inspection and repair of rotor blades in a technical article titled "Risk-Based Decision Modelling for Wind Turbine Leading Edge Erosion" [16]. We developed a taxonomy for gearbox damage, a framework for modelling vulnerabilities, and a procedure for conducting investigations [17]. We developed a decision support system that combines digital twins with advanced economic evaluation tools to enable continuous assessment of wind farm investments using the open-source platform DigiWind [18]. In order to consolidate the learning effects from these use cases, we are in the process of creating a Best Practices document.

The work done in 2025 benefits Task participants by delivering shared standards, tools, and collaborative platforms that improve data interoperability, digital decision-making, and innovation capacity. By advancing FAIR data practices, ontologies, digital twin methodologies, and cooperative research, it accelerates more efficient and reliable wind farm planning and operation, reduces costs and technical risks, and supports a skilled digital workforce. These outcomes benefit society through more affordable and resilient renewable energy systems, while contributing to environmental protection and climate mitigation by enabling faster deployment, optimised performance, and longer lifetimes of wind energy assets.

Highlights from 2025

  • Our "Recommended Practices on Creating and Publishing Ontologies for Wind Energy" was submitted to the ExCo for review in February 2026 (draft here [4]). This Recommended Practice first introduces ontologies as tools that allow experts to represent wind energy domain knowledge and outlines several use cases. It details a collaborative, community-driven approach for ontology development, drawing on the existing Methontology, Ontology Development 101, and BFO Community methodologies and leveraging open-source practices for sustainable development and community engagement.
  • Our paper "Understanding the effect of organisational culture on digitalisation" [12], based on a literature review and a multinational stakeholder survey, found that digital momentum is primarily driven by teams rather than entire organisations, with companies generally outperforming academic and research institutions. Key barriers include limited resources, unclear strategies, siloed structures, and skill gaps, while recommended actions focus on stronger leadership, targeted investment, improved communication, and structured support for innovation and training.
  • Working Group 4 published a paper "Risk-based decision modelling for wind turbine leading edge erosion" [16] in the journal Energies. It introduced a risk-based Bayesian decision making framework based on multiple data types that includes models on blade leading edge erosion damage propagation and blade repair cost, and demonstration of the methodology and modeling results for optimized blade inspection and repair.
  • Our three public webinars on the topics of knowledge graphs, immersive technologies and use cases attracted more than 50 participants, and our Annual General Meeting in Golden, CO, USA in June 2025 had 67 participants [18].

Next Steps

  • Publish the final version of the "Recommended Practices on Creating and Publishing Ontologies for Wind Energy".
  • Publish the final version of the "Joint Best Practice on Classifying and Valuing Digital Twins in Wind Energy".
  • Finish and publish the "Skills mapping for Wind Resource Assessment" report.
  • Publish the final version of the papers "Understanding the effect of organisational culture on digitalisation" and "Fostering open science through a digital open innovation platform – structural health monitoring case study".
  • Complete the Best Practices document for use cases.
  • Submission of and presentation of the New Task Proposal at the ExCo100 in May 2026, planned to start in October 2026.

References

  1. Clifton, A., Barber, S., Bray, A., Enevoldsen, P., Fields, J., Sempreviva, A. M., Williams, L., Quick, J., Purdue, M., Totaro, P., and Ding, Y.: Grand challenges in the digitalisation of wind energy, Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, 2023.
  2. Marykovskiy, Y., Clark, T., Day, J., Wiens, M., Henderson, C., Quick, J., Abdallah, I., Sempreviva, A. M., Calbimonte, J.-P., Chatzi, E., and Barber, S.: Knowledge engineering for wind energy, Wind Energ. Sci., 9, 883–917, https://doi.org/10.5194/wes-9-883-2024, 2024.
  3. Website with access to webinar recordings: https://iea-wind.org/task43/task-43-events/ (last accessed: 18.03.2026)
  4. Marykovskiy, Y., Jonsson, C., Farren, D., MaGill, C., Calbimonte, J.-P., & Barber, S. (2025). Recommended Practice: Ontology creation, publication, and maintenance for wind energy domain experts (Draft). Zenodo. https://doi.org/10.5281/zenodo.15308604
  5. Barber, S., Schindler, S., Jones, C., Rodríguez-Sánchez, P., Marykovskiy, Y., & RDA Wind Energy Community Standards WG. (2026). Best Practice Recommendations for Improving FAIR Data Maturity in Wind Energy (1.1.0). Zenodo. https://doi.org/10.15497/RDA00141
  6. WeDoWind Information Modelling Community: https://community.wedowind.ch/spaces/17168189/page (last accessed 18.03.2026)
  7. DITTA website on TechnoPortal: https://technoportal.hevs.ch/ontologies/DITTA/?p=classes&conceptid=root (last accessed: 18.03.2026)
  8. Barber, S., Marykovskiy, Y., Vallarampara, J., Hoghooghi, H., Hoi-Yin Yung, K., Khodam, A., Jonsson, C., Rodriguez, C., Wanasinghe, T. R., & Don, M. G. (2026). IEA Wind Task 43 Joint Best Practice on Classifying and Valuing Digital Twins in Wind Energy. Zenodo. https://doi.org/10.5281/zenodo.19081715
  9. The TechnoPortal: https://technoportal.hevs.ch (last accessed: 05.02.2026)
  10. Clark, T., Quick, J., Farren, D., Sempreviva, A. M., sheng, . shawn ., & Barber, S. (2025). IEA Wind Task 43 Technical Report - Evolving the Wind Energy Sector Towards Frictionless and Sustainable Data Usage. In IEA Wind TCP. Zenodo. https://doi.org/10.5281/zenodo.16811308
  11. Barber, S., Sempreviva, A. M., Clerc, J., and Hegemann, A.: Understanding organisational culture and digitalisation in the wind energy sector, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2025-159, in review, 2025.
  12. Barber, S., Panda, S., & Vallarampara, J. (2026). IEA Wind Task 43 report - A guide to improving digital organisational culture in wind energy. Zenodo. https://doi.org/10.5281/zenodo.18935810
  13. WeDoWind platform: https://community.wedowind.ch/ (last accessed: 18.03.2026)
  14. Barber, S., Wang, S., Pozo, F., Vidal, Y., Rodrigues Machado, M., Silva Rodrigues de Sousa, A. A., da Silva Coelho, J., Zhang, X., Hu, Y.-T., Noshadravan, A., Varouxis, T., Abdelhak, M., Ghiasi, R., and Malekjafarian, A.: Fostering open science through a digital open innovation platform – structural health monitoring case study, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2025-122, in review, 2025.
  15. Current version of the WRA Data Model: https://github.com/IEA-Task-43/digital_wra_data_standard/releases (last accessed: 06.02.2026)
  16. Nielsen JS, Clarke R, Paquette J, Farren D, Byrne A. Risk-Based Decision Modelling for Wind Turbine Leading Edge Erosion. Energies. 2025; 18(21):5784. https://doi.org/10.3390/en18215784
  17. Harikesan Baskaran. (2025, April 25). Assessing Gearbox Vulnerability to Wind Gusts: A Judgement-Based Approach. Zenodo. https://doi.org/10.5281/zenodo.15278197
  18. Kerr, A., Wiens, M., and Carriveau, R.: A Decision Support System for the Continuous Economic Evaluation of Wind Farms, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2025-175, in review, 2025.
  19. Report "Task 43 – Discovering synergies and making data AI-ready at the Annual General Meeting 2025": https://iea-wind.org/2025/06/30/task43-agm2025-report/

Task Contacts

Shawn Sheng, Operating Agent
shawn.sheng@nrel.gov

Sarah Barber, Task Manager
sarah.barber@ost.ch

Website:
iea-wind.org/task43/