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

Mathieu Pichault, Claire Vincent, Grant Skidmore and Jason Monty
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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

Energies, 2021, vol. 14, issue 9, 1-21

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.

Keywords: remote sensing; short-term forecast; wind power ramps (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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