Local linear regression with adaptive orthogonal fitting for the wind power application

For short-term forecasting of wind generation, a necessary step is to model the function for the conversion of meteorological variables (mainly wind speed) to power production. Such power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric approach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. They may lead inaccurate estimate of the true power curve, owing to the assumption such that a noise component is present on the response variable axis only. Therefore, this assumption is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive estimation method is introduced in order to accommodate nonstationarity. It has the additional benefit of lowering the computational costs of updating local coefficients every ...

• Type: Journal
• Year: 2008
• Where: SAC
• Authors: Pierre Pinson, Henrik Aalborg Nielsen, Henrik Madsen, Torben S. Nielsen
• Keywords: Applied Computing, Estimation Methods, Local Linear Regression, Power Curve, SAC 2008