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| Titel |
Deterministic prediction of surface wind speed variations |
| VerfasserIn |
G. V. Drisya, D. C. Kiplangat, K. Asokan, K. Satheesh Kumar |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
0992-7689
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| Digitales Dokument |
URL |
| Erschienen |
In: Annales Geophysicae ; 32, no. 11 ; Nr. 32, no. 11 (2014-11-19), S.1415-1425 |
| Datensatznummer |
250121132
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| Publikation (Nr.) |
 copernicus.org/angeo-32-1415-2014.pdf |
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| Zusammenfassung |
| Accurate prediction of wind speed is an important aspect of various tasks
related to wind energy management such as wind turbine predictive control and
wind power scheduling. The most typical characteristic of wind speed data is
its persistent temporal variations. Most of the techniques reported in the
literature for prediction of wind speed and power are based on statistical
methods or probabilistic distribution of wind speed data. In this paper we
demonstrate that deterministic forecasting methods can make accurate short-term predictions of wind speed using past data, at locations where the wind
dynamics exhibit chaotic behaviour. The predictions are remarkably accurate
up to 1 h with a normalised RMSE (root mean square error) of less than 0.02 and reasonably
accurate up to 3 h with an error of less than 0.06. Repeated application of
these methods at 234 different geographical locations for predicting wind
speeds at 30-day intervals for 3 years reveals that the accuracy of
prediction is more or less the same across all locations and time periods.
Comparison of the results with f-ARIMA model predictions shows that the
deterministic models with suitable parameters are capable of returning
improved prediction accuracy and capturing the dynamical variations of the
actual time series more faithfully. These methods are simple and
computationally efficient and require only records of past data for making
short-term wind speed forecasts within practically tolerable margin of
errors. |
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