Documents disponibles écrits par cet auteur

Forecasting the wind generation using a two-stage network based on meteorological information / Shu, Fan in IEEE transactions on energy conversion, Vol. 24 N° 2 (Juin 2009) 

Public ISBD [article]  in IEEE transactions on energy conversion > Vol. 24 N° 2 (Juin 2009) . - pp. 474 - 482 Titre : Forecasting the wind generation using a two-stage network based on meteorological information Type de document : texte imprimé Auteurs : Shu, Fan, Auteur ; Liao, James R., Auteur ; Yokoyama, Ryuichi, Auteur Année de publication : 2009 Article en page(s) : pp. 474 - 482 Note générale : energy conversion Langues : Anglais (eng) Mots-clés : Bayes  methods;  forecasting  theory;  meteorology;  pattern  clustering;  power  generation  scheduling;  power  markets;  regression  analysis;  wind  power;  wind  power  plants Résumé : This paper proposes a practical and effective model for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market. A novel forecasting model is developed based on indepth investigations of meteorological information. This model adopts a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression. The proposed structure is robust with different input data types and can deal with the nonstationarity of wind speed and generation series well. Once the network is trained, we can straightforward predict the 48-h ahead wind power generation. To demonstrate the effectiveness, the model is applied and tested on a 74-MW wind farm located in the southwest Oklahoma of the United States. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4757294&sortType%3Das [...] [article] Forecasting the wind generation using a two-stage network based on meteorological information [texte imprimé] / Shu, Fan, Auteur ; Liao, James R., Auteur ; Yokoyama, Ryuichi, Auteur . - 2009 . - pp. 474 - 482. energy conversion Langues : Anglais (eng) in IEEE transactions on energy conversion > Vol. 24 N° 2 (Juin 2009) . - pp. 474 - 482 Mots-clés : Bayes  methods;  forecasting  theory;  meteorology;  pattern  clustering;  power  generation  scheduling;  power  markets;  regression  analysis;  wind  power;  wind  power  plants Résumé : This paper proposes a practical and effective model for the generation forecasting of a wind farm with an emphasis on its scheduling and trading in a wholesale electricity market. A novel forecasting model is developed based on indepth investigations of meteorological information. This model adopts a two-stage hybrid network with Bayesian clustering by dynamics and support vector regression. The proposed structure is robust with different input data types and can deal with the nonstationarity of wind speed and generation series well. Once the network is trained, we can straightforward predict the 48-h ahead wind power generation. To demonstrate the effectiveness, the model is applied and tested on a 74-MW wind farm located in the southwest Oklahoma of the United States. En ligne : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4757294&sortType%3Das [...]