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Journals / Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi / 2014 / Cilt: 20 - Sayı: 5

Neural Prediction of Wind Blowing Durations Based on Average Wind Speeds for Akhisar Location

Akhisar Bölgesi için Ortalama Rüzgar Hızlarına Bağlı Rüzgar Esme Sürelerinin Yapay Sinir Ağları ile Tahmini

Raşit ATA
Pages
162–165
Publication Date
2014-05-01
DOI
—
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AI Summary & Key Takeaways

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

The results of ANN are compared with a conventional method in which Rayleigh distribution is employed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Renewable energy resources are widely preferred over conventional resources as they are environmentally favorable.
📊
Key Findings & Data:Wind energy is one of the important renewable energy resources and has been widely developed recently.
💡
Conclusion & Impact:The results of ANN are compared with a conventional method in which Rayleigh distribution is employed.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Rüzgar enerjisi · Rüzgar esme süresi · Hellmann katsayısı

Abstract

Renewable energy resources are widely preferred over conventional resources as they are environmentally favorable. Wind energy is one of the important renewable energy resources and has been widely developed recently. The energy produced from wind is dependent upon several factors. One of them is average wind speed and the other is wind blowing period. In this study, the wind blowing period is estimated based on annual average wind speed, Hellman coefficient and tower height using artificial neural networks (ANN). The results of ANN are compared with a conventional method in which Rayleigh distribution is employed.

Özet

Günümüzde enerjinin temiz, yerli ve yenilenebilir olması sadece ülkemizde değil tüm dünya ülkelerinde çoğunlukla kabul görmektedir. Alternatif ve temiz olan bu enerji kaynaklarından biri ve en önemlisi de rüzgar enerjisidir. Atmosferi kirleten fosil yakıtlarla karşılaştırıldığında rüzgar enerjisini elektrik enerjisine dönüştüren sistemlerin hızlı bir şekilde geliştiği ve kullanıldığı görülmektedir. Rüzgar türbinlerinden elde edilen elektrik enerjisi birkaç faktöre bağlı olarak değişir. Bu faktörlerden ikisi ortalama rüzgar hızı ve rüzgar esme süreleridir. Bu çalışmada, Akhisar bölgesi için yıllık ortalama rüzgar hızı, Hellmann katsayısı, kule yüksekliği gibi parametrelere bağlı rüzgar esme süreleri Yapay Sinir Ağları (YSA) ile analiz edilmektedir. Rüzgar esme süreleri analizinde Rayleigh dağılımı'nın kullanıldığı geleneksel yöntem(GY) ile YSA'nın karşılaştırılması yapılmaktadır.

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Cite This Article

ATA, R. (2014). Neural Prediction of Wind Blowing Durations Based on Average Wind Speeds for Akhisar Location. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 20 - Sayı: 5, 162–165. https://ajindex.com/articles/42734

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References (18)

  1. Monfared, M., Rastegar H., Kojabadi H. M., “A new strategy for wind speed forecasting using artificial intelligent methods”, Renewable Energy, 34: 845–848, 2009.
  2. Yerebakan, M., Rüzgar Enerjisi, İstanbul Ticaret Odası, Yayın No: 2001–33, 2001.
  3. Karadeli, S., Rüzgar Enerjisi, Temiz Enerji Vakfı, Kasım 2001.
  4. Wortman, A.J., Introduction to Wind Turbine Engineering, Butterworths, Boston, 1983.
  5. Kalogirou, S. A., “Applications of neural networks for energy systems”, Renewable Energy, 30 (7): 1075-1090, 2005.
  6. Kalogirou, S.A., “Artificial neural networks inrenewable energy systems applications: a review”, Renewable and Sustainable Energy Reviews, 5 (4): 373-401, 2001.
  7. Sreelakshmi, K., Ramakanthkumar, P., “Neural Networks for Short Term Wind Speed Prediction”, World Academy of Science, Engineering and Technology 18, 721-725, 2008.
  8. Kani, SAP, Ardehali, M.M., “Very short-term wind speed prediction: a new artificial neural network–Markov chain model” Energy Convers Manage, 52 (1): 738–45, 2011.
  9. Amjady, N., Keynia, F., Zareipour, H., “Short-term wind power forecasting using Ridgelet neural network” Electr Power Syst Res, 81 (12): 2099-107, 2011.
  10. Hui Liu, Chao Chen, Hong-qi Tian, Yan-fei Li., “A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks”, Renewable Energy, Vol. 48, pp. 545–556, 2012.
  11. Guo, Z.H, Wu, J, Lu, H.Y, Wang, J.Z., “A case study on a hybrid wind speed forecasting method using BP neural network.” Knowl Based Syst, 24 (7): 1048-56, 2011.
  12. Liu, H, Tian, H.Q, Chen, C, Li, Y.F., “A hybrid statistical method to predict wind speed and wind power”. Renew Energy 35 (8): 1857-6, 2010.
  13. Liu, H, Tian, HQ, Li, Y.F., “Comparison of two new ARIMA- ANN and ARIMA-Kalman hybrid methods for wind speed prediction.” Appl Energy, 98: 415–24, 2012.
  14. Li, G. and Shi, J., "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Vol. 87, pp. 2313-2320, 2010.
  15. Cadenas, E, Rivera, W., “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model.” Renew Energy, 35 (12): 2732-38, 2010.
  16. Guo, Z.H., Zhao, W.G., Lu, H.Y., Wang, J.Z., “Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model.” Renew Energy, 37 (1): 241-9, 2012.
  17. Bhaskar, K., Singh, S.N., “AWNN-assisted wind power forecasting using feedforward neural network.” IEEE Trans Sustain Energy, 3 (2): 306-15, 2012.
  18. Hui Liu, Hong-qi Tian, Di-fu Pan, Yan-fei Li., “Forecasting models