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Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR
| Content Provider | MDPI |
|---|---|
| Author | Pichault, Mathieu Vincent, Claire Skidmore, Grant Monty, Jason |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 2663 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en14092663 |
| Journal | Energies |
| Issue Number | 9 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-05-06 |
| Access Restriction | Open |
| Subject Keyword | Energies Marine Engineering Imaging Science Remote Sensing Short-term Forecast Wind Power Ramps |
| Content Type | Text |
| Resource Type | Article |
