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...
Main Authors: | , |
---|---|
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 |
id |
doaj-2a8f123d8ef941a0837851988f4b1993 |
---|---|
record_format |
Article |
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 |
_version_ |
1724353699993616384 |