Dergiler / International Journal of Environment and Geoinformatics / 2022 / Cilt: 9 - Sayı: 3

Prediction of Wind Speed by Using Chaotic Approach: A Case Study in Istanbul

Sayfa
48–56
Yayın Tarihi
2022-09-08
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In addition, a lesser correlation coefficient of 0.62 was found in the daily wind speed.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Renewable energy sources have gained great popularity due to the increasing importance given to a sustainable environment and economic development.
📊
Temel Bulgular & Veri:Because of the environmental friendliness of renewable energy compared to fossil fuels, the tendency and investments in this field have increased.
💡
Sonuç & Katkı:In addition, a lesser correlation coefficient of 0.62 was found in the daily wind speed.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Energy · Chaos Theory · Wind Speed

Abstract

Renewable energy sources have gained great popularity due to the increasing importance given to a sustainable environment and economic development. Because of the environmental friendliness of renewable energy compared to fossil fuels, the tendency and investments in this field have increased. Wind energy comes into prominence among renewable energy sources because of its potential power that is used in various areas currently. Wind energy having stochastic nature is more sensitive to the extreme values of wind speed. Therefore, in order to create wind energy effectively, an accurate wind speed forecast is needed. In this study, nonlinear dynamical system approaches have been implemented by using reconstructing of phase space based on specifying minimum embedded dimension and delay time. In order to find out performance, different error metrics (MSE, RMSE, MAE, and MAPE) have been implemented. According to results, RMSE has been found 0.47 and 0.85 in hourly and daily dataset, respectively. Also, the correlation coefficient between the measurement and the obtained data set was as high as 0.92 in the hourly wind variable. In addition, a lesser correlation coefficient of 0.62 was found in the daily wind speed.

🔖

Bu Makaleye Atıf Yap

KARA, Y., AYDIN, S., KARANFİL, E., ÖZGÜR, E. (2022). Prediction of Wind Speed by Using Chaotic Approach: A Case Study in Istanbul. International Journal of Environment and Geoinformatics, Cilt: 9 - Sayı: 3, 48–56. https://ajindex.com/articles/672481

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (11)

  1. Baydaroǧlu, Ö., Koçak, K. (2014). SVR-based prediction of evaporation combined with chaotic approach. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2013.11.008 Bergé, P., Pomeau, Y., Vidal, C. (1984).
  2. Order Within Chaos, John Wiley & Sons. Brzozowska, E., Borowska, M. (2016). Selection of phase space reconstruction parameters for EMG signals of the uterus. Studies in Logic, Grammar and Rhetoric. https://doi.org/10.1515/slgr-2016-0046
  3. Cassola, F., Burlando, M. (2012). Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output. Applied Energy. https://doi.org/10.1016/j.apenergy.2012.03.054
  4. Fraser, A. M., Swinney, H. L. (1986). Independent coordinates for strange attractors from mutual information. Physical Review A, 33(2): 1134-1140. https://doi.org/10.1103/PhysRevA.33.1134 Governorship of Istanbul, (n.d). Asya ve Avrupa’yı Birleştiren Şehir. Retrieved from: http://www.istanbul.gov.tr/
  5. Hegger, R., Kantz, H., Schreiber, T. (1999). Practical implementation of nonlinear time series methods: The TISEAN package. Chaos. https://doi.org/10.1063/1.166424
  6. Jamil, M., Zeeshan, M. (2019). A comparative analysis of ANN and chaotic approach-based wind speed prediction in India. Neural Computing and Applications. https://doi.org/10.1007/s00521-018-3513-2
  7. Karakasidis, T. E., Charakopoulos, A. (2009). Detection of low-dimensional chaos in wind time series. Chaos, Solitons and Fractals. https://doi.org/10.1016/j.chaos.2008.07.020
  8. Kennel, M. B., Brown, R., Abarbanel, H. D. I. (1992). Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A. https://doi.org/10.1103/PhysRevA.45.3403 Kliková, B., Raidl, A. (2011). Reconstruction of Phase Space of Dynamical Systems Using Method of Time Delay. Proceedings of the 20th Annual Conference of Doctoral Students - WDS 2011.
  9. Koçak, K., Şaylan, L., Eitzinger, J. (2004). Nonlinear prediction of near-surface temperature via univariate and multivariate time series embedding. Ecological Modelling. https://doi.org/10.1016/s0304-3800(03)00249-7
  10. Miraji, M. M. (2015). Chaotic Analysis of Wind Regime [MSc. Thesis, Istanbul Technical University]. Energy Institute. http://hdl.handle.net/11527/17987
  11. Özgür, E. (2010). Kaotik yaklaşımla rüzgar hızı öngörüsü [Wind speed prediction with chaotic approach]. [BSc. thesis, Istanbul Technical University]. Özgür, E., Koçak, K. (2011). Kaotik Yaklaşımla Kısa Vade Rüzgar Hızı Öngörüsü. V. Atmosfer Bil