TY  - JOUR
AU  - Petković, Dalibor
AU  - Nikolić, Vlastimir
AU  - Mitić, Vojislav V.
AU  - Kocić, Ljubiša
PY  - 2017
UR  - https://dais.sanu.ac.rs/123456789/4612
AB  - Since the wind speed fluctuation could cause large instability in wind energy systems it is crucial to develop a method for precise estimation of the wind speed fluctuation. Fractal interpolation of the wind speed could be used to improve the accuracy of the estimation of the wind speed fluctuation. Based on the self-similarity feature, the fractal interpolation could be established from internal to external interval. In this article fractal interpolation was used to improve the wind speed fluctuation estimation by soft computing methods. Artificial neural network (ANN) with different training algorithms were used in order to estimate the wind speed fluctuation based on the fractal interpolation
PB  - Elsevier
T2  - Flow Measurement and Instrumentation
T1  - Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms
SP  - 172
EP  - 176
VL  - 54
DO  - 10.1016/j.flowmeasinst.2017.01.007
UR  - https://hdl.handle.net/21.15107/rcub_dais_4612
ER  - 

@article{
author = "Petković, Dalibor and Nikolić, Vlastimir and Mitić, Vojislav V. and Kocić, Ljubiša",
year = "2017",
abstract = "Since the wind speed fluctuation could cause large instability in wind energy systems it is crucial to develop a method for precise estimation of the wind speed fluctuation. Fractal interpolation of the wind speed could be used to improve the accuracy of the estimation of the wind speed fluctuation. Based on the self-similarity feature, the fractal interpolation could be established from internal to external interval. In this article fractal interpolation was used to improve the wind speed fluctuation estimation by soft computing methods. Artificial neural network (ANN) with different training algorithms were used in order to estimate the wind speed fluctuation based on the fractal interpolation",
publisher = "Elsevier",
journal = "Flow Measurement and Instrumentation",
title = "Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms",
pages = "172-176",
volume = "54",
doi = "10.1016/j.flowmeasinst.2017.01.007",
url = "https://hdl.handle.net/21.15107/rcub_dais_4612"
}

Petković, D., Nikolić, V., Mitić, V. V.,& Kocić, L.. (2017). Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms. in Flow Measurement and Instrumentation
Elsevier., 54, 172-176.
https://doi.org/10.1016/j.flowmeasinst.2017.01.007
https://hdl.handle.net/21.15107/rcub_dais_4612

Petković D, Nikolić V, Mitić VV, Kocić L. Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms. in Flow Measurement and Instrumentation. 2017;54:172-176.
doi:10.1016/j.flowmeasinst.2017.01.007
https://hdl.handle.net/21.15107/rcub_dais_4612 .

Petković, Dalibor, Nikolić, Vlastimir, Mitić, Vojislav V., Kocić, Ljubiša, "Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms" in Flow Measurement and Instrumentation, 54 (2017):172-176,
https://doi.org/10.1016/j.flowmeasinst.2017.01.007 .,
https://hdl.handle.net/21.15107/rcub_dais_4612 .

TY  - JOUR
AU  - Petković, Dalibor
AU  - Nikolić, Vlastimir
AU  - Mitić, Vojislav V.
AU  - Kocić, Ljubiša
PY  - 2017
UR  - https://dais.sanu.ac.rs/123456789/2372
AB  - Since the wind speed fluctuation could cause large instability in wind energy systems it is crucial to develop a method for precise estimation of the wind speed fluctuation. Fractal interpolation of the wind speed could be used to improve the accuracy of the estimation of the wind speed fluctuation. Based on the self-similarity feature, the fractal interpolation could be established from internal to external interval. In this article fractal interpolation was used to improve the wind speed fluctuation estimation by soft computing methods. Artificial neural network (ANN) with different training algorithms were used in order to estimate the wind speed fluctuation based on the fractal interpolation
PB  - Elsevier
T2  - Flow Measurement and Instrumentation
T1  - Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms
SP  - 172
EP  - 176
VL  - 54
DO  - 10.1016/j.flowmeasinst.2017.01.007
UR  - https://hdl.handle.net/21.15107/rcub_dais_2372
ER  - 

@article{
author = "Petković, Dalibor and Nikolić, Vlastimir and Mitić, Vojislav V. and Kocić, Ljubiša",
year = "2017",
abstract = "Since the wind speed fluctuation could cause large instability in wind energy systems it is crucial to develop a method for precise estimation of the wind speed fluctuation. Fractal interpolation of the wind speed could be used to improve the accuracy of the estimation of the wind speed fluctuation. Based on the self-similarity feature, the fractal interpolation could be established from internal to external interval. In this article fractal interpolation was used to improve the wind speed fluctuation estimation by soft computing methods. Artificial neural network (ANN) with different training algorithms were used in order to estimate the wind speed fluctuation based on the fractal interpolation",
publisher = "Elsevier",
journal = "Flow Measurement and Instrumentation",
title = "Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms",
pages = "172-176",
volume = "54",
doi = "10.1016/j.flowmeasinst.2017.01.007",
url = "https://hdl.handle.net/21.15107/rcub_dais_2372"
}

Petković, D., Nikolić, V., Mitić, V. V.,& Kocić, L.. (2017). Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms. in Flow Measurement and Instrumentation
Elsevier., 54, 172-176.
https://doi.org/10.1016/j.flowmeasinst.2017.01.007
https://hdl.handle.net/21.15107/rcub_dais_2372

Petković D, Nikolić V, Mitić VV, Kocić L. Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms. in Flow Measurement and Instrumentation. 2017;54:172-176.
doi:10.1016/j.flowmeasinst.2017.01.007
https://hdl.handle.net/21.15107/rcub_dais_2372 .

Petković, Dalibor, Nikolić, Vlastimir, Mitić, Vojislav V., Kocić, Ljubiša, "Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorothms" in Flow Measurement and Instrumentation, 54 (2017):172-176,
https://doi.org/10.1016/j.flowmeasinst.2017.01.007 .,
https://hdl.handle.net/21.15107/rcub_dais_2372 .

TY  - JOUR
AU  - Nikolić, Vlastimir
AU  - Mitić, Vojislav V.
AU  - Kocić, Ljubiša
AU  - Petković, Dalibor
PY  - 2017
UR  - https://dais.sanu.ac.rs/123456789/15994
AB  - Fluctuation of wind speed affects wind energy systems since the potential wind power is proportional the cube of wind speed. Hence precise prediction of wind speed is very important to improve the performances of the systems. Due to unstable behavior of the wind speed above different terrains, in this study fractal characteristics of the wind speed series were analyzed. According to the self-similarity characteristic and the scale invariance, the fractal extrapolate interpolation prediction can be performed by extending the fractal characteristic from internal interval to external interval. Afterward neuro-fuzzy technique was applied to the fractal data because of high nonlinearity of the data. The neuro-fuzzy approach was used to detect the most important variables which affect the wind speed according to the fractal dimensions. The main goal was to investigate the influence of terrain roughness length and different heights of the wind speed on the wind speed prediction.
PB  - Springer London
T2  - Knowledge and Information Systems
T1  - Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique
SP  - 255
EP  - 265
VL  - 52
IS  - 1
DO  - 10.1007/s10115-016-1006-0
UR  - https://hdl.handle.net/21.15107/rcub_dais_15994
ER  - 

@article{
author = "Nikolić, Vlastimir and Mitić, Vojislav V. and Kocić, Ljubiša and Petković, Dalibor",
year = "2017",
abstract = "Fluctuation of wind speed affects wind energy systems since the potential wind power is proportional the cube of wind speed. Hence precise prediction of wind speed is very important to improve the performances of the systems. Due to unstable behavior of the wind speed above different terrains, in this study fractal characteristics of the wind speed series were analyzed. According to the self-similarity characteristic and the scale invariance, the fractal extrapolate interpolation prediction can be performed by extending the fractal characteristic from internal interval to external interval. Afterward neuro-fuzzy technique was applied to the fractal data because of high nonlinearity of the data. The neuro-fuzzy approach was used to detect the most important variables which affect the wind speed according to the fractal dimensions. The main goal was to investigate the influence of terrain roughness length and different heights of the wind speed on the wind speed prediction.",
publisher = "Springer London",
journal = "Knowledge and Information Systems",
title = "Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique",
pages = "255-265",
volume = "52",
number = "1",
doi = "10.1007/s10115-016-1006-0",
url = "https://hdl.handle.net/21.15107/rcub_dais_15994"
}

Nikolić, V., Mitić, V. V., Kocić, L.,& Petković, D.. (2017). Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique. in Knowledge and Information Systems
Springer London., 52(1), 255-265.
https://doi.org/10.1007/s10115-016-1006-0
https://hdl.handle.net/21.15107/rcub_dais_15994

Nikolić V, Mitić VV, Kocić L, Petković D. Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique. in Knowledge and Information Systems. 2017;52(1):255-265.
doi:10.1007/s10115-016-1006-0
https://hdl.handle.net/21.15107/rcub_dais_15994 .

Nikolić, Vlastimir, Mitić, Vojislav V., Kocić, Ljubiša, Petković, Dalibor, "Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique" in Knowledge and Information Systems, 52, no. 1 (2017):255-265,
https://doi.org/10.1007/s10115-016-1006-0 .,
https://hdl.handle.net/21.15107/rcub_dais_15994 .