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

fYear

2010

fDate

21-23 June 2010

Firstpage

46

Lastpage

50

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICGCS.2010.5543100

Filename

5543100