Wind speed probability density estimation using root-transformed local linear regression

doi:10.1016/j.enconman.2019.111889

Wind speed probability density estimation using root-transformed local linear regression

2019-11-01

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DOI：10.1016/j.enconman.2019.111889

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Despite the many attractive features of wind-based renewables, the intermittency of wind generation stands out as a critical challenge. An accurate and reliable nonparametric wind speed probability density model is proposed in this paper based on the application of local linear regression in tandem with a root transformation method, introduced here for the first time. The proposed root-transformed local linear regression approach provides a robust estimate of wind speed distribution and produces more accurate results than kernel density estimation models. The performance of the root-transformed local linear regression estimator is assessed via comparisons with three popular parametric models (Rayleigh, Weibull and Gaussian distributions), two additional parametric models (Birnbaum-Saunders and Nakagami distributions) recently suggested for wind speed probability density estimation, and two nonparametric kernel density estimation models using two standard goodness-of-fit hypothesis tests (Chi-squared and Kolmogorov-Smirnov), coefficient of determination and two error metrics (root mean square error and mean absolute error). Results confirm the accuracy of the proposed root-transformed local linear regression estimator for modelling wind speed probability density, with substantial improvements in all error metrics over parametric and kernel density estimation models.

Keywords:

Adaptive estimation

Nonparametric regression

Parametric models

Probability density estimation

Wind speed models

Wind speed probability density estimation using root-transformed local linear regression

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