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Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets

Author

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  • Emily Cowin

    (Civil Engineering, Monash University, Clayton 3800, Australia
    These authors contributed equally to this work.)

  • Changlong Wang

    (Civil Engineering, Monash University, Clayton 3800, Australia
    These authors contributed equally to this work.)

  • Stuart D. C. Walsh

    (Civil Engineering, Monash University, Clayton 3800, Australia
    These authors contributed equally to this work.)

Abstract

Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements.

Suggested Citation

  • Emily Cowin & Changlong Wang & Stuart D. C. Walsh, 2023. "Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets," Energies, MDPI, vol. 16(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3404-:d:1121964
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    References listed on IDEAS

    as
    1. Gruber, Katharina & Regner, Peter & Wehrle, Sebastian & Zeyringer, Marianne & Schmidt, Johannes, 2022. "Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas," Energy, Elsevier, vol. 238(PA).
    2. Jensen, Cathrine Ulla & Panduro, Toke Emil & Lundhede, Thomas Hedemark & Nielsen, Anne Sofie Elberg & Dalsgaard, Mette & Thorsen, Bo Jellesmark, 2018. "The impact of on-shore and off-shore wind turbine farms on property prices," Energy Policy, Elsevier, vol. 116(C), pages 50-59.
    3. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    4. Arash Khatibi & Stefan Krauter, 2021. "Validation and Performance of Satellite Meteorological Dataset MERRA-2 for Solar and Wind Applications," Energies, MDPI, vol. 14(4), pages 1-17, February.
    5. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
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    1. Dario Maradin & Bojana Olgić Draženović & Saša Čegar, 2023. "The Efficiency of Offshore Wind Energy Companies in the European Countries: A DEA Approach," Energies, MDPI, vol. 16(9), pages 1-16, April.

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