A statistical analysis of wind speed data used in installation of wind energy conversion systems

Akpınar, Ebru Kavak; Akpınar, Sinan

doi:10.1016/j.enconman.2004.05.002

Cited by 132 publications

(90 citation statements)

References 23 publications

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“…The data were collected from Brown et al 1984;Daniel and Chen 1991;Garcia et al 1998;Justus et al 1978;Kavak Akpinar and Akpinar 2005;Nfaoui et al 1996;Torres et al 2005; J. r. a. Benth and Benth 2010;Celik 2004;Tuller and Brett 1984).…”

Section: Wind Speed Modelingmentioning

confidence: 99%

Wind Derivatives: Modeling and Pricing

Alexandridis

Zapranis

2012

Comput Econ

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Abstract.Wind is considered to be a free, renewable and environmentally friendly source of energy. However, wind farms are exposed to excessive weather risk since the power production depends on the wind speed and the wind direction. This risk can be successfully hedged using a financial instrument called weather derivatives. In this study the dynamics of the wind generating process are modeled using a non-parametric non-linear wavelet network. Our model is validated in New York. The proposed methodology is compared against alternative methods, proposed in prior studies. We find that wavelet networks can model the wind process very well and consequently they constitute an accurate and efficient tool for wind derivatives pricing. Finally, we provide the pricing equations for wind futures.

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Section: Wind Speed Modelingmentioning

confidence: 99%

Wind Derivatives: Modeling and Pricing

Alexandridis

Zapranis

2012

Comput Econ

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“…13,25 The most probable wind speed (V mp ) which represents the most frequent wind speed and wind speed carrying maximum energy (V max,E ) are two significant wind speeds for wind energy estimation. 1,26,27 These statistical characteristics are not directly connected to wind energy, but the efficiency of a wind turbine conversion system is closely related to these parameters and especially V max,E , which should be as close as possible to the rated wind speed of the system.…”

Section: Introductionmentioning

confidence: 99%

Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function

Bagiorgas

Mihalakakou

Rehman

et al. 2012

Journal of Renewable and Sustainable Energy

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This paper utilizes three hourly measured values of wind speed and direction from seven buoys data collection stations in Aegean Sea to study the wind speed and power characteristics applying the Weibull shape and scale parameters. Specifically, the site dependent, annual and monthly mean patterns of mean wind speed, Weibull parameters, frequency distribution, most probable wind speed, maximum energy carrying wind speed, wind power density and wind energy density characteristics have been studied. The Weibull distribution was found to represent the wind speed distribution with more than 90% accuracy in most of the cases. Slightly decreasing trends were observed in annual mean wind speed values at Lesvos and increasing at Mykonos. The mean values of wind speed, scale parameter, most probable wind speed, maximum energy carrying wind speed, wind power and wind energy density values showed higher values during winter time and lower in summer time. Mykonos was found to be the best site from wind power harnessing point of view. Moreover, the correlation between the percentages of times the wind speed was above cut-in-speed and the measured mean wind speed for the three selected sites and the correlation between the aforementioned percentages and the scale parameter c were examined and were found linear.

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“…Weibull in wind data description is frequently preferred as a statistical distribution describing data [15]. Besides, Akpinar and Akpinar [24] showed that over a period of 12 months, Weibull distribution gave improved power density estimates in all months compared to Rayleigh based on a study conducted in Turkey. Furthermore, they stated that, for the entire year, measured monthly probability density is better in fitting Weibull distribution than Rayleigh distribution.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…To find k and c, analytical and empirical methods shown below such as Justus formulas are used [18], [24]: …”

Section: Theoretical Backgroundmentioning

confidence: 99%

Güney Batı Karadeniz Kıyıları Boyunca Rüzgar Güç Potansiyelinin Değerlendirilmesi

Akpınar

2016

SAUFenBilDer

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Information about wind characteristic together with the analysis of its potential are a key step when it comes to planning and performance estimation of wind energy projects. In the current study, hence, a statistical analysis on wind energy potential and wind characteristics over the coasts of the South Western Black sea is carried out. The aim is to determine fields where wind force is strong and consistent for energy production along south western coasts of the Black Sea. For this purpose, seven meteorological stations (Kumköy, Şile, Akçakoca, Zonguldak, Amasra, Cide, İnebolu) from west to east are selected, and 10 year wind data acquired from TSMS (Turkish Meteorological Service) measured hourly at a height of 10 m were used. Annual, seasonal, monthly and diurnal average wind speed and power density variations are evaluated at all stations, after which conclusions on dominant wind directions and frequency distributions at all sites are made. Based on overall results, Amasra and İnebolu indicated the highest potential of wind power among the other sites with Amasra having about 159 W/m 2 maximum yearly average wind power density and about 1392 kWh/m 2 /year maximum yearly average energy density.Keywords: wind speeds, wind power potential, the south western Black Sea coasts Güney Batı Karadeniz kıyıları boyunca rüzgar güç potansiyelinin değerlendirilmesi ÖZ Bir bölgenin rüzgar karakteristiğinin belirlenmesi ve onun potansiyelinin farklı açılardan analizi, rüzgar enerji projelerinin planlanması ve performans tahminlerinin yapılabilmesi için gerekli olan en önemli adımdır. Bu çalışmada, Karadeniz'in güney batı kıyılarının rüzgar karakteristiğinin ve rüzgar enerji potansiyelinin detaylı istatistiksel analizi gerçekleştirilmiştir. Çalışmadaki hedef, Karadeniz'in Türkiye kıyıları boyunca enerji üretimi için rüzgar gücü yüksek olan uygun alanları belirlemektir. Bu hedef için, bahsedilen kıyı şeridinde rüzgar ölçümlerinin gerçekleştirildiği 7 meteorolojik istasyon seçilmiş ve istatistiksel analiz için Türkiye Meteoroloji Genel Müdürlüğü tarafından arşivlenmiş 10 metre yükseklikteki saatlik rüzgar verisi (hızı ve yönü) 10 yıllık süre boyunca temin edilmiştir. Bütün istasyonlarda hakim rüzgar yönleri ve frekans dağılımları belirlendikten sonra yıllık, mevsimlik, aylık ve günlük ortalama rüzgar hız ve güç yoğunluk değişimleri de irdelenmiştir. Bütün sonuçlara dayanarak, Amasra ve İnebolu lokasyonlarının diğer alanlara kıyasla en yüksek rüzgar güç potansiyeline sahip oldukları, Amasra'nın yaklaşık 159 W/m 2 maksimum yıllık ortalama rüzgar güç yoğunluğuna ve yaklaşık 1392 kWh/m 2

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A statistical analysis of wind speed data used in installation of wind energy conversion systems

Cited by 132 publications

References 23 publications

Wind Derivatives: Modeling and Pricing

Wind Derivatives: Modeling and Pricing

Wind power potential assessment for seven buoys data collection stations in Aegean Sea using Weibull distribution function

Güney Batı Karadeniz Kıyıları Boyunca Rüzgar Güç Potansiyelinin Değerlendirilmesi

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