Title :
Wind prediction using complex augmented adaptive filters
Author :
Kuh, Anthony ; Manloloyo, Christopher ; Corpuz, Raynel ; Kowahl, Nathan
Author_Institution :
Dept. Electr. Eng., Univ. of Hawaii, Honolulu, HI, USA
Abstract :
In this paper we discuss a new set of nonlinear adaptive filters based on kernel methods and compare them to the least mean square (LMS) and recursive least squares (RLS) adaptive filters. In recent years a new class of nonlinear kernel adaptive filters have been developed that tradeoff performance for complexity including the Kernel LMS (KLMS) and Kernel RLS (KRLS) algorithms. Earlier work discussed a complex augmented implementation of the kernel algorithms. This paper continues this discussion and compares the performance and complexity of the algorithms for wind time series prediction.
Keywords :
adaptive filters; computational complexity; nonlinear filters; recursive filters; time series; wind power plants; algorithm complexity; complex augmented adaptive filters; kernel LMS algorithms; kernel RLS algorithms; kernel methods; least mean square adaptive filters; nonlinear kernel adaptive filters; recursive least square adaptive filters; wind power generation; wind prediction; wind time series prediction; Adaptive filters; Kernel; Least squares approximation; Least squares methods; Machine learning algorithms; Signal processing algorithms; Wind energy; Wind forecasting; Wind speed; Wind turbines; Wind forecasting; complex augmented kernels; recursive kernel algorithms;
Conference_Titel :
Green Circuits and Systems (ICGCS), 2010 International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-6876-8
Electronic_ISBN :
978-1-4244-6877-5
DOI :
10.1109/ICGCS.2010.5543100
