2021
DOI: 10.1177/0309524x211038547
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Ultra-short-term wind speed prediction based on variational mode decomposition and optimized extreme learning machine

Abstract: The main purpose of this paper is to improve the prediction accuracy of ultra-short-term wind speed. It is difficult to predict the ultra-short-term wind speed because of its unstable, non-stationary and non-linear. Aiming at the unstable and non-stationary characteristics of ultra-short-term wind speed, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed data, and a series of stable and stationary components with different frequencies are obtained. The extre… Show more

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Cited by 5 publications
(1 citation statement)
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“…In order to reduce the accuracy impact caused by the random selection of parameters, Feng et al [11] used the Moore-Penrose inverse restricted Boltzmann strategy to recursively adjust the weights in the ELM. Lian et al [12] used the whale optimization algorithm to optimize the extreme learning machine to improve the regression performance. Jian et al [13] used the PSO algorithm to optimize the parameters in the ELM model and applied it to the color classification of sunglasses lenses, which achieved good results.…”
Section: Introductionmentioning
confidence: 99%
“…In order to reduce the accuracy impact caused by the random selection of parameters, Feng et al [11] used the Moore-Penrose inverse restricted Boltzmann strategy to recursively adjust the weights in the ELM. Lian et al [12] used the whale optimization algorithm to optimize the extreme learning machine to improve the regression performance. Jian et al [13] used the PSO algorithm to optimize the parameters in the ELM model and applied it to the color classification of sunglasses lenses, which achieved good results.…”
Section: Introductionmentioning
confidence: 99%