Entropy approach for volatility of wind energy

In this study, we give the practice of entropy in wind energy. Firstly, we fit marginal distributions to each of the variables and later demonstrate the notion of entropy to perform a comparison the wind energy data of the stations (Bursa, Elazig, Istanbul, Mugla, Rize, Tokat, Van, and Zonguldak) th...

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Main Authors: Calik Sinan, Metin Karakas Ayse
Format: Article
Language:English
Published: VINCA Institute of Nuclear Sciences 2019-01-01
Series:Thermal Science
Subjects:
Online Access:http://www.doiserbia.nb.rs/img/doi/0354-9836/2019/0354-98361900346C.pdf
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spelling doaj-2a8f123d8ef941a0837851988f4b19932021-01-02T13:50:00ZengVINCA Institute of Nuclear SciencesThermal Science0354-98362019-01-0123Suppl. 61863187410.2298/TSCI190101346C0354-98361900346CEntropy approach for volatility of wind energyCalik Sinan0Metin Karakas Ayse1Department of Statistics, Faculty of Science, Firat University, Elazig, TurkeyDepartment of Statistics, Faculty of Art and Science, Bitlis Eren University, Bitlis, TurkeyIn this study, we give the practice of entropy in wind energy. Firstly, we fit marginal distributions to each of the variables and later demonstrate the notion of entropy to perform a comparison the wind energy data of the stations (Bursa, Elazig, Istanbul, Mugla, Rize, Tokat, Van, and Zonguldak) that have been examined in a period 2015-2018. The results of probability distribution fitting to these wind energy variables show that the wind energy time series of Bursa, Elazıg, Istanbul, Mugla, Rize, Tokat, Van, and Zonguldak are best resubmitted by Gamma Burr and Lognormal distributions. Later, we calculate Shannon entropy for several various values, Tsallis entropy, Renyi entropy, and the approximate entropy. We form calculation outcomes with these entropies for daily dats.http://www.doiserbia.nb.rs/img/doi/0354-9836/2019/0354-98361900346C.pdfshannon entropytsallis entropyapproximate entropywind energyrényi entropy
collection DOAJ
language English
format Article
sources DOAJ
author Calik Sinan
Metin Karakas Ayse
spellingShingle Calik Sinan
Metin Karakas Ayse
Entropy approach for volatility of wind energy
Thermal Science
shannon entropy
tsallis entropy
approximate entropy
wind energy
rényi entropy
author_facet Calik Sinan
Metin Karakas Ayse
author_sort Calik Sinan
title Entropy approach for volatility of wind energy
title_short Entropy approach for volatility of wind energy
title_full Entropy approach for volatility of wind energy
title_fullStr Entropy approach for volatility of wind energy
title_full_unstemmed Entropy approach for volatility of wind energy
title_sort entropy approach for volatility of wind energy
publisher VINCA Institute of Nuclear Sciences
series Thermal Science
issn 0354-9836
publishDate 2019-01-01
description In this study, we give the practice of entropy in wind energy. Firstly, we fit marginal distributions to each of the variables and later demonstrate the notion of entropy to perform a comparison the wind energy data of the stations (Bursa, Elazig, Istanbul, Mugla, Rize, Tokat, Van, and Zonguldak) that have been examined in a period 2015-2018. The results of probability distribution fitting to these wind energy variables show that the wind energy time series of Bursa, Elazıg, Istanbul, Mugla, Rize, Tokat, Van, and Zonguldak are best resubmitted by Gamma Burr and Lognormal distributions. Later, we calculate Shannon entropy for several various values, Tsallis entropy, Renyi entropy, and the approximate entropy. We form calculation outcomes with these entropies for daily dats.
topic shannon entropy
tsallis entropy
approximate entropy
wind energy
rényi entropy
url http://www.doiserbia.nb.rs/img/doi/0354-9836/2019/0354-98361900346C.pdf
work_keys_str_mv AT caliksinan entropyapproachforvolatilityofwindenergy
AT metinkarakasayse entropyapproachforvolatilityofwindenergy
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