Examination of persistence properties of wind speed records using detrended fluctuation analysis

Koçak, Kasım

doi:10.1016/j.energy.2009.08.006

Cited by 56 publications

(33 citation statements)

References 31 publications

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“…In contrast, only a handful of studies [16][17][18][19][20][21][22][23][24] delved into characterizing the mesoscale range (approximately, sub-hourly to sub-daily time-scale). They analyzed observational datasets from field sites around the world (e.g., China, France, New Zealand, Italy, the Netherlands, Turkey, and the USA).…”

Section: Introductionmentioning

confidence: 99%

Buoyancy effects on the scaling characteristics of atmospheric boundary-layer wind fields in the mesoscale range

Kiliyanpilakkil¹,

Basu²,

Ruiz-Columbie³

et al. 2015

Phys. Rev. E

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We have analyzed long-term wind speed time-series from five field sites up to a height of 300 m from the ground. Structure function-based scaling analysis has revealed that the scaling exponents in the mesoscale regime systematically depend on height. This anomalous behavior is likely caused by the buoyancy effects. In the framework of the extended self-similarity, the relative scaling exponents portray quasi-universal behavior.

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Section: Introductionmentioning

confidence: 99%

Buoyancy effects on the scaling characteristics of atmospheric boundary-layer wind fields in the mesoscale range

Kiliyanpilakkil¹,

Basu²,

Ruiz-Columbie³

et al. 2015

Phys. Rev. E

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“…Koçak (2009) evaluated wind speed persistence using detrended fluctuation analysis. The European wind atlas is the most complete mapping methodology in wind power meteorology searches.…”

Section: Introductionmentioning

confidence: 99%

Determination of the Representative Time Horizons for Short-term Wind Power Prediction by Using Artificial Neural Networks

Izgi¹,

Öztopal²,

Yerli³

et al. 2014

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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Wind power is one of the major renewable energy sources, and this source has reached to compete with conventional energies. Wind speed has very complex variations during different time horizons. Prediction of wind speed shows some uncertainties depending on atmospheric parameters, such as temperature, pressure, solar irradiation, and relative humidity. Additionally, wind turbines could not generate electricity at all wind speed values that are less than cut-in and greater than cut-out. These conditions add new uncertainties to the prediction of this meteorological parameter. In this case, wind power prediction from generated electricity data will be better than direct wind speed or other meteorological parameters. Generally, in engineering applications wind power prediction is based on hourly mean, in other words, one hour time horizon data. This time horizon is accepted as a reference and representative but physically this horizon does not represent data homogeneity and should be changed depending on location and time of year. The main aim of this article is to determine the highest representative time horizon to predict wind power in a considered system. First, artificial neural networks methodology is applied to generated power with a 1.5-kW wind turbine by using 1 and 60 minutes average data. It is seen that 35 minutes time horizon gives the best representative scale for wind power prediction in April and 15 minutes in August, respectively. During these months, errors between measured and testing values are decreased to 9.72 and 3.62%, respectively, for the mentioned time horizons. Especially, it is seen that mean values that are evaluated from less than 8-10 minutes and greater than 40 minutes time horizon data give high errors. In other words, using very short time horizons of power data cause high prediction variations and, as a result of these deviations, wind power prediction shows high errors.

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“…In the current literature, determination of long-term persistence is another important research area. In this area, autocorrelation function, fluctuation analysis (FA), de-trended fluctuation analysis (DFA), spectral analysis, Hurst method, wavelet method and fractal dimension are e96 F. M. KORKMAZ AND K. KOÇAK used frequently in order to determine the long-term persistence properties of a given variable (Govindan and Kantz, 2004;Kavasseri, 2004;Kavasseri and Nagarajan, 2005;Harrouni and Guessoum, 2009;Koçak, 2009;Orun and Koçak, 2009;Telesca et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Climatology of wind speed persistence over Turkey

Korkmaz¹,

Koçak

2017

Intl Journal of Climatology

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It is a well‐known fact that, sustainability is very important issue in the field of energy production of any kind. Wind speed persistence, as a climatological indicator, is one of the most important components of wind phenomenon which is closely related with sustainability of wind energy production. The most suitable areas for wind energy production are the areas with high wind speed persistence. On the other hand, there are a few studies on how to measure persistence of wind properly. In this study, two novel methods used to investigate persistence characteristics of a given area were applied to Turkey's wind speed data. The results have revealed some important characteristics of persistence throughout Turkey. According to these results western parts of Anatolia have highest persistence. The second region that has high persistence is inner parts of Anatolia because the topography of the Aegean region which enables wind to penetrate along long distances from the coast to the inner parts of Anatolia. All of the results obtained from the study are in accordance with the observations.

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Examination of persistence properties of wind speed records using detrended fluctuation analysis

Cited by 56 publications

References 31 publications

Buoyancy effects on the scaling characteristics of atmospheric boundary-layer wind fields in the mesoscale range

Buoyancy effects on the scaling characteristics of atmospheric boundary-layer wind fields in the mesoscale range

Determination of the Representative Time Horizons for Short-term Wind Power Prediction by Using Artificial Neural Networks

Climatology of wind speed persistence over Turkey

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