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access icon free Combinatorial method for bandwidth selection in wind speed kernel density estimation

  • Author(s): Omar El-Dakkak 1, 2 ; Samuel Feng 3 ; Maisam Wahbah 4 ; Tarek H.M. EL-Fouly 4 ; Bashar Zahawi 4
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  • Source: Volume 13, Issue 10, 29 July 2019, p. 1670 – 1680
    DOI:  10.1049/iet-rpg.2018.5643 , Print ISSN 1752-1416, Online ISSN 1752-1424
© The Institution of Engineering and Technology
Received 26/07/2018, Accepted 02/04/2019, Revised 09/01/2019, Published 11/04/2019

Accurate estimation of wind speed probability density at a given site is crucial in maximising the yield of a wind farm. This goal calls for devising probabilistic models with adaptive algorithms that accurately fit wind speed distributions. In this study, a non-parametric combinatorial method is implemented for obtaining an accurate non-parametric kernel density estimation (KDE)-based statistical model of wind speed, in which the selection of the bandwidth parameter is optimised concerning mean integrated absolute error ( error) between the true and hypothesised densities. The proposed model is compared with three popular parametric models and Rule of Thumb-based KDE model using standard goodness-of-fit and statistical tests. Results confirm the suitability of KDE methods for wind speed modelling and the accuracy of the proposed implemented combinatorial method. It is worthwhile mentioning that the implemented procedure is adaptive (i.e. data driven) and robust.

Inspec keywords: statistical testing; combinatorial mathematics; statistical distributions; wind power; estimation theory; wind power plants

Other keywords: wind speed probability density; popular parametric models; hypothesised densities; wind speed distributions; standard goodness-of-fit and statistical tests; devising probabilistic models; nonparametric combinatorial method; wind speed modelling; KDE methods; Thumb-based KDE model; mean integrated absolute error; accurate estimation; bandwidth selection; true densities; wind farm; accurate nonparametric kernel density estimation-based statistical model; given site; adaptive algorithms; wind speed kernel density estimation; bandwidth parameter

Subjects: Combinatorial mathematics; Wind power plants; Other topics in statistics; Energy resources

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