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ItemNo Preview AvailableHimawari-8 GeoCat 1.0.3 Australian Domain Level 1 v1.0Lopez-Bravo, C ; Vincent, C ; Huang, Y ( 2021-03-15)
ItemShort-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDARPichault, M ; Vincent, C ; Skidmore, G ; Monty, J (MDPI, 2021-05-01)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.
ItemCharacterisation of intra-hourly wind power ramps at the wind farm scale and associated processesPichault, M ; Vincent, C ; Skidmore, G ; Monty, J (COPERNICUS GESELLSCHAFT MBH, 2021-01-19)One of the main factors contributing to wind power forecast inaccuracies is the occurrence of large changes in wind power output over a short amount of time, also called “ramp events”. In this paper, we assess the behaviour and causality of 1183 ramp events at a large wind farm site located in Victoria (southeast Australia). We address the relative importance of primary engineering and meteorological processes inducing ramps through an automatic ramp categorisation scheme. Ramp features such as ramp amplitude, shape, diurnal cycle and seasonality are further discussed, and several case studies are presented. It is shown that ramps at the study site are mostly associated with frontal activity (46 %) and that wind power fluctuations tend to plateau before and after the ramps. The research further demonstrates the wide range of temporal scales and behaviours inherent to intra-hourly wind power ramps at the wind farm scale.