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Renewables: Share data on wind energy

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  • Andrew Kusiak

    (Andrew Kusiak is professor of mechanical and industrial engineering, and director of the Intelligent Systems Laboratory, at the University of Iowa, Iowa City, Iowa, USA.)

Abstract

Giving researchers access to information on turbine performance would allow wind farms to be optimized through data mining, says Andrew Kusiak.

Suggested Citation

  • Andrew Kusiak, 2016. "Renewables: Share data on wind energy," Nature, Nature, vol. 529(7584), pages 19-21, January.
    • Handle: RePEc:nat:nature:v:529:y:2016:i:7584:d:10.1038_529019a
    DOI: 10.1038/529019a
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    Citations

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    Cited by:

    1. Gonzalo Gil & Aitor Arnaiz & Mariví Higuero & Francisco Javier Diez & Eduardo Jacob, 2022. "Context-Aware Policy Analysis for Distributed Usage Control," Energies, MDPI, vol. 15(19), pages 1-25, September.
    2. Chatterjee, Joyjit & Dethlefs, Nina, 2021. "Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Mohammad AlMuhaini & Abass Yahaya & Ahmed AlAhmed, 2023. "Distributed Generation and Load Modeling in Microgrids," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    4. Liang, Guoyuan & Su, Yahao & Wu, Xinyu & Ma, Jiajun & Long, Huan & Song, Zhe, 2023. "Abnormal data cleaning for wind turbines by image segmentation based on active shape model and class uncertainty," Renewable Energy, Elsevier, vol. 216(C).
    5. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    6. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
    7. Jaume Manero & Javier Béjar & Ulises Cortés, 2019. "“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting," Energies, MDPI, vol. 12(12), pages 1-20, June.

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