Prediction of wind speed with non-linear autoregressive (NAR) neural networks [Rüzgâr Hizinin Dogrusal Olmayan Otoregresif Sinir Aglari ile Tahmini]

In this study, the wind speed prediction model is created that gives a minimum error for different hidden layer neuron numbers and delay step numbers. Using the one-minute time series, the prediction of the next wind speed is performed with the NAR neural network model. The predicted values of wind speed obtained are compared with predicted values of wind speed obtained with filter methods. For different window functions and lengths, wind speed prediction is made using filters with different weight coefficients. For the number of hidden layer neurons is 14 and the number of delay steps is 10, MAE, MSE and RMSE values are calculated as 0.0315, 0.0019, 0.0445, respectively, with NAR neural network. It is seen that the proposed method for the wind speed dataset has a higher prediction performance than thefilter methods. © 2017 IEEE.

Eser Adı
[dc.title]
Prediction of wind speed with non-linear autoregressive (NAR) neural networks [Rüzgâr Hizinin Dogrusal Olmayan Otoregresif Sinir Aglari ile Tahmini]
Yazar
[dc.contributor.author]
Karasu, Seçkin
Yazar
[dc.contributor.author]
Altan, Aytaç
Yazar
[dc.contributor.author]
Saraç, Zehra
Yazar
[dc.contributor.author]
Hacıoğlu, Rıfat
Yayın Yılı
[dc.date.issued]
2017
Yayıncı
[dc.publisher]
Institute of Electrical and Electronics Engineers Inc.
Yayın Türü
[dc.type]
proceedings
Açıklama
[dc.description]
25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703
Özet
[dc.description.abstract]
In this study, the wind speed prediction model is created that gives a minimum error for different hidden layer neuron numbers and delay step numbers. Using the one-minute time series, the prediction of the next wind speed is performed with the NAR neural network model. The predicted values of wind speed obtained are compared with predicted values of wind speed obtained with filter methods. For different window functions and lengths, wind speed prediction is made using filters with different weight coefficients. For the number of hidden layer neurons is 14 and the number of delay steps is 10, MAE, MSE and RMSE values are calculated as 0.0315, 0.0019, 0.0445, respectively, with NAR neural network. It is seen that the proposed method for the wind speed dataset has a higher prediction performance than thefilter methods. © 2017 IEEE.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
tur
Konu Başlıkları
[dc.subject]
Artificial Neural Network
Konu Başlıkları
[dc.subject]
Nonlinear Auto-Regressive (NAR)
Konu Başlıkları
[dc.subject]
Prediction Method
Konu Başlıkları
[dc.subject]
Signal Processing
Konu Başlıkları
[dc.subject]
Wind Speed
Künye
[dc.identifier.citation]
Karasu, S., Altan, A., Saraç, Z. ve Hacioğlu, R. (2017). Prediction of wind speed with non-linear autoregressive (Nar) neural networks. 2017 25th Signal Processing and Communications Applications Conference (SIU) içinde (ss. 1-4). 2017 25th Signal Processing and Communications Applications Conference (SIU), sunulmuş bildiri. doi:10.1109/SIU.2017.7960507
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
Dergi Adı
[dc.relation.journal]
2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1109/SIU.2017.7960507
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/7120
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433
09.12.2022 tarihinden bu yana
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1
09.12.2022 tarihinden bu yana
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16 Temmuz 2024 22:31
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prediction values different number neural methods network obtained numbers hidden predicted calculated respectively proposed dataset higher performance thefilter method compared created minimum neuron one-minute series performed neurons filter window functions lengths filters weight coefficients
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