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

Year 2018, Volume: 13 Issue: 2, 98 - 107, 22.04.2018

Mustafa Yasin Erten , Hüseyin Aydilek , Ertuğrul Çam , Nihat İnanç

Abstract

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.

Keywords

Wind Energy, Time Series, Forecasting, ARMA, ES, ES

References

• [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.