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Dergiler / Sigma Journal of Engineering and Natural Sciences / 2026 / Volume: 36 Issue: 2

ESTIMATING WIND ENERGY POTENTIAL WITH PREDICTING BURR LSM PARAMETERS: A DIFFERENT APPROACH

Bayram Köse(KARABUK UNIVERSITY)0000-0003-0256-5921
Murat Düz(KARABUK UNIVERSITY)0000-0003-2387-4045
M. Tahir Güneşer(KARABUK UNIVERSITY)0000-0003-3502-2034
Ziyaddin Recebli(KARABUK UNIVERSITY)0000-0003-3110-9423
Sayfa
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Yayın Tarihi
2018-06-01
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The results show that the Burr LSM is better than the others for seven of eight weather stations in terms of the power density.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Estimating wind energy potential and wind speed frequency are important for planning wind energy conversion plants.
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Temel Bulgular & Veri:Regarding the comparison results, the performance of designed estimation model (Burr LSM) is higher than the Weibull distribution models, especially for the locations with higher average wind speeds.
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Sonuç & Katkı:The results show that the Burr LSM is better than the others for seven of eight weather stations in terms of the power density.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:: Burr probability distribution function · forecasting · modelling

Abstract

Estimating wind energy potential and wind speed frequency are important for planning wind energy conversion plants. Probability distribution functions are utilized to model wind speed distributions. In this study, an estimation was model designed by using the least squares method to predict the wind speed density with the Burr distribution, which has not been studied before. To confirm this model, the annual data of eight different weather stations were analysed, and the results were compared with the Weibull distribution model, which is the most popular one in the literature. For predicting the parameters of both models least square method and maximum likely methods were used. Regarding the comparison results, the performance of designed estimation model (Burr LSM) is higher than the Weibull distribution models, especially for the locations with higher average wind speeds. The results show that the Burr LSM is better than the others for seven of eight weather stations in terms of the power density.

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Bu Makaleye Atıf Yap

Köse, B., Düz, M., Güneşer, M. T., Recebli, Z. (2018). ESTIMATING WIND ENERGY POTENTIAL WITH PREDICTING BURR LSM PARAMETERS: A DIFFERENT APPROACH. Sigma Journal of Engineering and Natural Sciences, Volume: 36 Issue: 2. https://ajindex.com/articles/1632849

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