| Titre : | Short-horizon prediction of wind power : a data-driven approach (2011) |
| Auteurs : | Kusiak, A., Auteur ; Zijun Zhang, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | IEEE transactions on energy conversion (Vol. 25, N° 4, Décembre 2010) |
| Article en page(s) : | pp. 1112 - 1122 |
| Note générale : | energy conversion |
| Langues : | Anglais |
| Tags : | Data mining ; neural nets ; power engineering computing ; prediction theory ; statistical analysis ; wind plants |
| Résumé : | This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm. |
| En ligne : | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5451084&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A5635301%29 |
