DocumentCode
1922603
Title
Data mining techniques for very short term prediction of wind power
Author
Vargas, Luis ; Paredes, Gonzalo ; Bustos, Gonzalo
Author_Institution
Dept. of Electr. Eng., Univ. of Chile, Santiago, Chile
fYear
2010
fDate
1-6 Aug. 2010
Firstpage
1
Lastpage
7
Abstract
This paper presents a comparison of data mining techniques for wind power forecasting in a time frame out to 15 minutes ahead. The forecasting is focused on the power generated by the wind farms and the power changes are predicted by using multivariate time series models ARMA, focus time-delay neural network (FTDNN) and a phenomenological model of the turbines. All these models are tested with real data of a 18 MW wind farm.
Keywords
autoregressive moving average processes; data mining; load forecasting; neural nets; power engineering computing; time series; wind power plants; wind turbines; ARMA; FTDNN; data mining techniques; focus time delay neural network; multivariate time series models; turbines; wind farms; wind power forecasting; wind power generation; Artificial neural networks; Forecasting; Mathematical model; Predictive models; Wind forecasting; Wind power generation; Wind turbines; Data Mining; Wind Power Forecasting;
fLanguage
English
Publisher
ieee
Conference_Titel
Bulk Power System Dynamics and Control (iREP) - VIII (iREP), 2010 iREP Symposium
Conference_Location
Rio de Janeiro
Print_ISBN
978-1-4244-7466-0
Electronic_ISBN
978-1-4244-7465-3
Type
conf
DOI
10.1109/IREP.2010.5563273
Filename
5563273
Link To Document