Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-947-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-947-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2023
Research article |
| 07 Jun 2023
| 07 Jun 2023
Grand challenges in the digitalisation of wind energy
Andrew Clifton
CORRESPONDING AUTHOR
Stuttgart Wind Energy at the Institute of Aircraft Design, University of Stuttgart, Stuttgart, Germany
now at: enviConnect, TTI GmbH, 70569 Stuttgart, Germany
Sarah Barber
Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Andrew Bray
MXV Ventures, Oakland, California, USA
now at: Aurora Energy Research, Oakland, California, USA
Peter Enevoldsen
Centre for Energy Technologies, Aarhus University, Aarhus, Denmark
Jason Fields
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, USA
Anna Maria Sempreviva
Department of Wind Energy, DTU, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Lindy Williams
Computational Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
Julian Quick
The Paul M. Rady Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA
now at: Department of Wind Energy and Energy Systems, DTU, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mike Purdue
NRG Sytems, Hinesburg, VT, USA
Philip Totaro
IntelStor LLC, Houston, TX, USA
Yu Ding
Department of Industrial and Systems Engineering, Texas A & M University, College Station, TX, USA
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Cited
21 citations as recorded by crossref.
- Data imputation for SCADA data using Graph Neural Networks F. Hammer & S. Barber
- Knowledge engineering for wind energy Y. Marykovskiy et al.
- Deep generative models in energy system applications: Review, challenges, and future directions X. Zhang et al.
- Digitalization in the Renewable Energy Sector M. El Zein & G. Gebresenbet
- Improving data sharing in practice – power curve benchmarking case study S. Barber & Y. Ding
- Wind Energy in Transition: Development, Socio-Economic Impacts, and Policy Challenges in Europe H. Wojtaszek et al.
- Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification A. Gijón et al.
- Improving data sharing in wind energy - structural health monitoring case study S. Barber et al.
- DigiWind-An Open-Source Digital Twin Framework for Wind Energy Systems M. Wiens et al.
- Deep Learning Approaches for Offshore Wind Turbine Load Prediction: A Comparative Study Using Simulation, Measurement, and Transfer Learning D. Liu et al.
- Datasets for wind energy forecasting applications: A comprehensive review and benchmarking perspective R. Akhtar et al.
- A GIS-portal platform from the data perspective to energy hub digitalization solutions- A review and a case study M. Majidi Nezhad et al.
- Global dynamics and local capabilities: how ICT convergence in wind energy impacts innovation in semi-peripheral countries? M. Castelao Caruana & R. Vidosa
- Industry 4.0 digital technologies for the advancement of renewable energy: Functions, applications, potential and challenges G. Naeem et al.
- Architecting a digital twin for wind turbine rotor blade aerodynamic monitoring Y. Marykovskiy et al.
- Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning L. Jenkel et al.
- Using machine learning methods for long-term technical and economic evaluation of wind power plants A. Omidkar et al.
- A Study on Fault Detection of Wind Turbine Bearings Using 2D CNN T. Kang et al.
- Investigation of Wind Turbine Static Yaw Error Based on Utility-Scale Controlled Experiments D. Astolfi et al.
- Intelligent optimisation for sustainable development of onshore wind farm battery energy storage systems: A systematic review M. Gwabavu et al.
- Technical and economic challenges for floating offshore wind deployment in Italy and in the Mediterranean Sea L. Serri et al.
21 citations as recorded by crossref.
- Data imputation for SCADA data using Graph Neural Networks F. Hammer & S. Barber
- Knowledge engineering for wind energy Y. Marykovskiy et al.
- Deep generative models in energy system applications: Review, challenges, and future directions X. Zhang et al.
- Digitalization in the Renewable Energy Sector M. El Zein & G. Gebresenbet
- Improving data sharing in practice – power curve benchmarking case study S. Barber & Y. Ding
- Wind Energy in Transition: Development, Socio-Economic Impacts, and Policy Challenges in Europe H. Wojtaszek et al.
- Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification A. Gijón et al.
- Improving data sharing in wind energy - structural health monitoring case study S. Barber et al.
- DigiWind-An Open-Source Digital Twin Framework for Wind Energy Systems M. Wiens et al.
- Deep Learning Approaches for Offshore Wind Turbine Load Prediction: A Comparative Study Using Simulation, Measurement, and Transfer Learning D. Liu et al.
- Datasets for wind energy forecasting applications: A comprehensive review and benchmarking perspective R. Akhtar et al.
- A GIS-portal platform from the data perspective to energy hub digitalization solutions- A review and a case study M. Majidi Nezhad et al.
- Global dynamics and local capabilities: how ICT convergence in wind energy impacts innovation in semi-peripheral countries? M. Castelao Caruana & R. Vidosa
- Industry 4.0 digital technologies for the advancement of renewable energy: Functions, applications, potential and challenges G. Naeem et al.
- Architecting a digital twin for wind turbine rotor blade aerodynamic monitoring Y. Marykovskiy et al.
- Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning L. Jenkel et al.
- Using machine learning methods for long-term technical and economic evaluation of wind power plants A. Omidkar et al.
- A Study on Fault Detection of Wind Turbine Bearings Using 2D CNN T. Kang et al.
- Investigation of Wind Turbine Static Yaw Error Based on Utility-Scale Controlled Experiments D. Astolfi et al.
- Intelligent optimisation for sustainable development of onshore wind farm battery energy storage systems: A systematic review M. Gwabavu et al.
- Technical and economic challenges for floating offshore wind deployment in Italy and in the Mediterranean Sea L. Serri et al.
Saved (final revised paper)
Latest update: 06 May 2026
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 challengesaround 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.
Wind energy creates huge amounts of data, which can be used to improve plant design, raise...
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