Journals / Çukurova Üniversitesi Mühendislik-Mimarlik Fakültesi Dergisi / 2017 / Cilt: 32 - Sayı: 2

Zaman Serisi Analiz Metotları Kullanılarak Rüzgâr Hızının Tahmin Edilmesi

Wind Speed Forecasting using Time Series Analysis Methods

Pages
161–172
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

The low performance measure values seen in results indicate that the methods used in this study can be forecast for wind speed

📌 3 Key Takeaways

🎯
Objective & Hypothesis:As a natural, non-consumable, clean and sustainable energy resource, wind energy is becoming crucial throughout the world.
📊
Key Findings & Data:In this study, several time series analysis methods for wind energy were compared considering long-termmonthly-average wind speed data between the years of 1960 and 2014 at nine meteorological stations throughout five geographical areas in Turkey.
💡
Conclusion & Impact:The low performance measure values seen in results indicate that the methods used in this study can be forecast for wind speed
🔬Design:Research Article
📍Scope:2014 Verileri
⚙️Variables:Domain Parameters

Abstract

As a natural, non-consumable, clean and sustainable energy resource, wind energy is becoming crucial throughout the world. Forecasting wind speed is noteworthy to design and install wind power stations. In this study, several time series analysis methods for wind energy were compared considering long-termmonthly-average wind speed data between the years of 1960 and 2014 at nine meteorological stations throughout five geographical areas in Turkey. The low performance measure values seen in results indicate that the methods used in this study can be forecast for wind speed

Özet

Doğal, tükenmeyen, temiz ve sürdürülebilir bir enerji kaynağı olduğundan rüzgâr enerjisi dünyada önem kazanmaktadır. Rüzgâr enerjisi istasyonlarının tasarlanması ve kurulması için rüzgâr hızı tahmini önemlidir. Bu çalışmada, Türkiye'deki beş farklı coğrafi bölge ve dokuz meteorolojik istasyondan 1960 ve 2014 yılları arasındaki uzun dönemli aylık ortalama rüzgâr hızı verileri dikkate alınarak rüzgâr enerjisi için farklı zaman serisi analizi metotları karşılaştırılmıştır. Çalışmanın sonucunda elde edilen düşük performans ölçüm değerleri, rüzgâr hızı tahminleri için bu çalışmada ele alınan metotların kullanılabileceğini göstermektedir

🔖

Cite This Article

AKCAN, S. (2017). Zaman Serisi Analiz Metotları Kullanılarak Rüzgâr Hızının Tahmin Edilmesi. Çukurova Üniversitesi Mühendislik-Mimarlik Fakültesi Dergisi, Cilt: 32 - Sayı: 2, 161–172. https://ajindex.com/articles/202362

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (14)

  1. Wagner, H.J., Mathur, J., 2012. Introduction to Wind Energy Systems: Basics, Technology and Operation. Springer Science & Business Media
  2. Lun, I.Y., Lam, J.C., 2000. A Study of Weilbull Parameters using Long-term Wind Observations. Renewable Energy 20(2), 154- 153
  3. Ewing, B.T., Kruse, J.B., Schroeder, J.L., Smith, D.A., 2007. Time Series Analysis of Wind Speed using VAR and the Generalized Impulse Response Technique. Journal of Wind Engineering and Industrial Aerodynamics 95, 209- 219
  4. Togrul, I.T., Ertekin, C., 2011. A Statistical Investigation on the Wind Energy Potential of Turkey’s Geographical Regions. Energy Sources 33, 1399-1421
  5. Su, C., Quan, J., Fu, Y., 2012. Correlation Analysis for Wind Speed and Failure Rate of Wind Turbines using Time Series Approach. Journal of Renewable and Sustainable Energy 4, 032301
  6. Assareh, E., Behrang, M.A., Ghalambaz, M., Noghrehabadi, A.R., Ghanbarzadeh, A., 2012. An Analysis of Wind Speed Prediction using Artificial Neural Networks: A Case Study in Manjil Iran. Energy Sources 34, 636- 644
  7. Shu, Z.R., Li, Q.S., Chan, P.W., 2015. Statistical Analysis of Wind Characteristics and Wind Energy Potential in Hong Kong. Energy Conversion and Management 101, 644- 657
  8. Zhang, Z., Zhang, R., Fang, D., Wang, J., 2015. Prediction Study and Application of Wind Power Development Based on Filtering Error Threshold. Environmental Progress and Sustainable Energy 34 (5), 1536- 1546
  9. Saberivahidaval, M., Hajjam, S., 2015. Comparison Between Performances of Different Neural Networks for Wind Speed Forecasting in Payam Airport, Iran. Environmental Progress and Sustainable Energy 34 (4), 1191- 1196
  10. Zuluaga, C.D., Alvarez, M.A., Giraldo, E., 2015. Short-term Wind Speed Prediction Based on Robust Kalman Filtering: an Experimental Comparison. Applied Energy 156, 321-330
  11. Santamaria-Bonfil, G., Reyes-Ballesteros, A., Gershenson, C., 2016. Wind Speed Forecasting for Wind Farms: A Method Based on Support Vector Regression. Renewable Energy 85, 790- 809
  12. Mohandes, M., Rehman, S., Abido, M., Badran, S., 2016. Convertible Wind Energy Based on Predicted Wind Speed at Hub-height, Energy Sources 38(1), 140-148
  13. Ambach, D., Croonenbroeck, C., 2016. Space Time Short to Medium Term Wind Speed Forecasting. Statistical Methods and Applications 25(1), 5- 20
  14. Doucoure, B., Agbossou, K., Cardenas, A., 2016. Time Series Prediction using Artificial Wavelet Neural Network and Multi-resolution Analysis: Application