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Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR

Author

Listed:
  • Mathieu Pichault

    (Department of Mechanical Engineering, The University of Melbourne, Parkville 3010, Australia)

  • Claire Vincent

    (School of Geography, Earth and Atmospheric Sciences, The University of Melbourne, Parkville 3010, Australia)

  • Grant Skidmore

    (Department of Mechanical Engineering, The University of Melbourne, Parkville 3010, Australia)

  • Jason Monty

    (Department of Mechanical Engineering, The University of Melbourne, Parkville 3010, Australia)

Abstract

It remains unclear to what extent remote sensing instruments can effectively improve the accuracy of short-term wind power forecasts. This work seeks to address this issue by developing and testing two novel forecasting methodologies, based on measurements from a state-of-the-art long-range scanning Doppler LiDAR. Both approaches aim to predict the total power generated at the wind farm scale with a five minute lead time and use successive low-elevation sector scans as input. The first approach is physically based and adapts the solar short-term forecasting approach referred to as “smart-persistence” to wind power forecasting. The second approaches the same short-term forecasting problem using convolutional neural networks. The two methods were tested over a 72 day assessment period at a large wind farm site in Victoria, Australia, and a novel adaptive scanning strategy was implemented to retrieve high-resolution LiDAR measurements. Forecast performances during ramp events and under various stability conditions are presented. Results showed that both LiDAR-based forecasts outperformed the persistence and ARIMA benchmarks in terms of mean absolute error and root-mean-squared error. This study is therefore a proof-of-concept demonstrating the potential offered by remote sensing instruments for short-term wind power forecasting applications.

Suggested Citation

  • Mathieu Pichault & Claire Vincent & Grant Skidmore & Jason Monty, 2021. "Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR," Energies, MDPI, vol. 14(9), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2663-:d:549597
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    References listed on IDEAS

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