Data-Adaptive Censoring for Short-Term Wind Speed Predictors Based on MLP, RNN, and SVM
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-Inst Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study introduces novel short-term wind speed predictors based on multilayer perceptron (MLP), recurrent neural network (RNN), and support vector machine (SVM) by combining them with the data-adaptive censoring (DAC) strategy. Taking into account the multistep ahead prediction mode, we design a DAC strategy based on the least mean square (LMS) algorithm, which iteratively obtains a new training dataset consisting of the most informative input-output wind data from all training set for MLP, RNN, and SVM structures. This enables us to censor less informative training data with high accuracy and thereby the training costs of the MLP, RNN, and SVM are reduced without a considerably adverse effect on their prediction performances in testing processes. The conducted simulation results on real-life large-scale short-term wind speed data verify the mentioned attractive features of the proposed predictors.
Açıklama
Anahtar Kelimeler
Training, Wind speed, Prediction algorithms, Support vector machines, Predictive models, Wind farms, Testing, Data-adaptive censoring (DAC), least mean square (LMS), multilayer perceptron (MLP), recurrent neural networks (RNNs), support vector machine (SVM), wind speed
Kaynak
Ieee Systems Journal
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
16
Sayı
3
