COMPARING TIME SERIES FORECASTING METHODS TO ESTIMATE WIND SPEED IN KIRIKKALE REGION

Due to the non-storable nature of electric energy, short-term and long-term electricity generation and consumption forecast are critical to keeping electricity market in balance. In addition, the production estimate of wind energy is parallel to the estimate of wind speed. Since wind speed forecasts includes seasonal and time-dependent trends, time series forecasting methods produce successful results in wind energy forecasting. However, choosing the most appropriate time series forecasting method for short-term and long-term production forecasts is of special importance. In this study, short-term and long-term wind speed estimations were made for the wind turbine at Kırıkkale University by using Exponential Smoothing (ES) and ARMA (Auto Regressive Moving Average) methods. The most suitable methods for forecasting short-term and long-term wind speed have been determined with the obtained results.

___

  • [1] Monteiro, C., Bessa, R., Miranda, V., Botterud, A., Wang, J., and Conzelmann, G., (2009). Wind Power Forecasting: State-of-the-art 2009 (No. ANL/DIS-10-1). Argonne National Laboratory (ANL).
  • [2] Datta, R. and Ranganathan, V.T., (2002). Variable-speed Wind Power Generation Using Doubly Fed Wound Rotor Induction Machine-a Comparison with Alternative Schemes. IEEE transactions on Energy conversion, 17(3):414-421.
  • [3] Okumus, I. and Dinler, A., (2016). Current Status of Wind Energy Forecasting and a Hybrid Method for Hourly Predictions. Energy Conversion and Management, 123, 362-371.
  • [4] Asghar, A.B. and Liu, X., (2017). Adaptive Neuro-fuzzy Algorithm to Estimate Effective Wind Speed and Optimal Rotor Speed for Variable-Speed Wind Turbine. Neurocomputing.
  • [5] Nikolić, V., Motamedi, S., Shamshirband, S., Petković, D., Ch, S., and Arif, M., (2016). Extreme Learning Machine Approach for Sensorless Wind Speed Estimation. Mechatronics, 34, 78-83.
  • [6] Cheng, W.Y., Liu, Y., Bourgeois, A.J., Wu, Y., and Haupt, S.E., (2017). Short-term Wind Forecast of a Data Assimilation/Weather Forecasting System with Wind Turbine Anemometer Measurement Assimilation. Renewable Energy, 107, 340-351.
  • [7] Kantar, Y.M., Usta, I., Arik, I., and Yenilmez, I., (2017). Wind Speed Analysis Using the Extended Generalized Lindley Distribution. Renewable Energy.
  • [8] Wang, J., Hu, J., and Ma, K., (2016). Wind Speed Probability Distribution Estimation and Wind Energy Assessment. Renewable and Sustainable Energy Reviews, 60, 881-899.
  • [9] Xiao, L., Wang, J., Dong, Y., and Wu, J., (2015). Combined Forecasting Models for Wind Energy Forecasting: A case study in China. Renewable and Sustainable Energy Reviews, 44, 271-288.
  • [10] Esen, H., Ozgen, F., Esen, M., and Sengur, A., (2009). Artificial Neural Network and Wavelet Neural Network Approaches for Modelling of a Solar Air Heater. Expert systems with applications, 36(8), 11240-11248.
  • [11] Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., and Yan, Z., (2009). A review on the Forecasting of Wind Speed and Generated Power. Renewable and Sustainable Energy Reviews, 13(4), 915-920.
  • [12] Rahman, M.A. and Rahim, A.H.M.A. (2016, May). Performance Evaluation of ANN and ANFIS Based Wind Speed Sensor-Less MPPT Controller. In Informatics, Electronics and Vision (ICIEV), 2016 5th International Conference on (pp:542-546). IEEE.
  • [13] Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M., (2015). Time Series Analysis: Forecasting and Control. John Wiley & Sons. [14] Gardner, E.S., (1985). Exponential Smoothing: The State of the Art. Journal of forecasting, 4(1):1-28.
  • [15] National Institute of Standards and Technology. 6.4.3. What is Exponential Smoothing?. [online] Available at: http://www.itl.nist.gov/div898/handbook/pmc/section4/pmc43.htm [Accessed 19 Sep. 2017].
  • [16] Kotz, S. and Nadarajah, S., (2000). Extreme Value Distributions: Theory and Applications. World Scientific.
  • [17] Jia, G., Li, D., Yao, L., and Zhao, P., (2016, June). An Improved Artificial Bee Colony-BP neural Network Algorithm in The Short-Term Wind Speed Prediction. In Intelligent Control and Automation (WCICA), 2016 12th World Congress on (pp:2252-2255). IEEE.
  • [18] Zhang, J., Wei, Y., Tan, Z. F., Ke, W., and Tian, W., (2017). A Hybrid Method for Short-Term Wind Speed forecasting. Sustainability, 9(4):596.
Technological Applied Sciences-Cover
  • Başlangıç: 2009
  • Yayıncı: E-Journal of New World Sciences Academy
Sayıdaki Diğer Makaleler

KANAL KESİT GEOMETRİSİ ÜÇGEN OLAN PEM YAKIT HÜCRESİNİN PERFORMANSININ İNCELENMESİ

Muhammet ÖZDOĞAN, Aydın DURMUŞ

AISI 1040 ÇELİKLERİ İÇİN VİCKERS MİKROSERTLİK–UYGULANAN YÜK ARASINDAKİ İLİŞKİNİN İNCELENMESİ

Erdal KARADENİZ, Metecan İŞÇİOĞLU

THE EFFECTS OF RENEWABLE ENERGY SOURCES ON VOLTAGE STABILITY

Salih Tosun, Ali Öztürk, Uğur Hasırcı

ÜÇ BOYUTLU ZEMİN-YAPI SİSTEMLERİNDE AKTİF YÖNTEMLERLE TİTREŞİM İZOLASYONU SAĞLANABİLİRLİĞİNİN ARAŞTIRILMASI

Oğuz Akın DÜZGÜN, Ahmet Şahin ZAİMOĞLU

ULTRASONİK GAZ GİDERME YÖNTEMİ İLE METAL KALİTESİNİN ARTIRILMASI

Çağlar Yüksel, Muhammet Cemal Öztürk, Yusuf Basri Balcı, Hüseyincan Eker, Mustafa Çiğdem, Derya Dışpınar, Uğur Aybarç

DISCRETE AND CONTINUOUS DESIGN OPTIMIZATION OF TOWER STRUCTURES USING THE JAYA ALGORITHM

S. Özgür DEĞERTEKİN, Luciano LAMBERTİ, İ. Behram UGUR

HAZARDS IN KIDS FURNITURE

Zübeyde BÜLBÜL, Mehmet Özgür KUŞCUOĞLU, Sait Dündar SOFUOĞLU, Emine Seda ERDİNLER

SIC TAKVIYELİ ALÜMINYUM ESASLI KOMPOZITLERIN MEKANİK ÖZELLİKLERİNİN VE MİKRO YAPISININ İNCELENMESİ

Mahmut Can Şenel, Mevlüt Gürbüz, Erdem Koç

COMPARING TIME SERIES FORECASTING METHODS TO ESTIMATE WIND SPEED IN KIRIKKALE REGION

Mustafa Yasin ERTEN, Hüseyin AYDİLEK, Ertuğrul Çam, Nihat İnanç

TÜRKİYE’DEKİ TÜM İLLER İÇİN OPTİMUM YALITIM KALINLIĞININ NÜMERİK İNCELENMESİ

Burak TÜRKAN, Ahmet Serhan CANBOLAT, Akın Burak ETEMOĞLU, Ömer KAYNAKLI