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This paper proposes a statistical method for 1-6 hour-ahead prediction of hourly mean wind speed and direction to better forecast the power produced by wind turbines, an increasingly important component of power system operation. The wind speed and direction are modelled via the magnitude and phase of a complex vector containing measurements from multiple geographic locations. The predictor is derived from the spatio-temporal covariance which is estimated at regular time intervals from a subset of the available training data, the wind direction of which lies within a sliding range of angles centred on the most recent measurement of wind direction. This is a generalisation of regime-switching type approaches which train separate predictors for a few fixed regimes. The new predictor is tested on the Hydra dataset of wind across the Netherlands and compared to persistence and a cyclo-stationary Wiener filter, a state-of-the-art spatial predictor of wind speed and direction. Results show that the proposed technique is able to predict the wind vector more accurately than these benchmarks on dataset containing 4 to 27 sites, with greater accuracy for larger datasets.
|Number of pages||5|
|Publication status||Published - Jul 2014|
|Event||2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014 - Durham, United Kingdom|
Duration: 7 Jul 2014 → 10 Jul 2014
|Conference||2014 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2014|
|Abbreviated title||PMAPS 2014|
|Period||7/07/14 → 10/07/14|
- load forecasting
- spatiotemporal phenomena
- wind turbines
- complex vector phase
- power system operation
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