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Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2018 / Cilt: 36 - Sayı: 2

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

Bayram KÖSE
Murat DÜZ
M. Tahir GÜNEŞER
Ziyaddin RECEBLİ
Pages
389–404
Publication Date
2018-06-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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 Key Takeaways

🎯
Objective & Hypothesis:Estimating wind energy potential and wind speed frequency are important for planning wind energy conversion plants.
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Key Findings & Data: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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Conclusion & Impact:The results show that the Burr LSM is better than the others for seven of eight weather stations in terms of the power density.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:: 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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Cite This Article

KÖSE, B., DÜZ, M., GÜNEŞER, M. T., RECEBLİ, Z. (2018). ESTIMATING WIND ENERGY POTENTIAL WITH PREDICTING BURR LSM PARAMETERS: A DIFFERENT APPROACH. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 36 - Sayı: 2, 389–404. https://ajindex.com/articles/419175

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References (1)

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