2018
DOI: 10.1016/j.renene.2017.10.111
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Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network

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Cited by 99 publications
(36 citation statements)
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“…First step consists in wind speed prediction followed by the usage of the turbine power curve for wind power determination [4]. For the former class of methods, the precision of the forecast is significantly influenced by the process of wind speed prediction [8], or if it is also considered the high degree of variability of wind-to-power curve, both terms play a key role in limited predictability of wind power generation [9].…”
Section: Wind Energy Production Planningmentioning
confidence: 99%
“…First step consists in wind speed prediction followed by the usage of the turbine power curve for wind power determination [4]. For the former class of methods, the precision of the forecast is significantly influenced by the process of wind speed prediction [8], or if it is also considered the high degree of variability of wind-to-power curve, both terms play a key role in limited predictability of wind power generation [9].…”
Section: Wind Energy Production Planningmentioning
confidence: 99%
“…Considering these problems, this paper uses a simple and effective modal fluctuation method to determine the number of K modes. The algorithm flow is as follows [30]:…”
Section: Determine Vmd Parametersmentioning
confidence: 99%
“…As a result, the prediction result of EMD has a large error. Therefore, IMFs decomposed by VMD are more suitable for the establishment of hybrid forecasting model than IMFs decomposed by EMD [40].…”
Section: Data Decomposition Preprocessingmentioning
confidence: 99%