A Comparison of Support Vector Regression and Multivariable Grey Model for Short-Term Wind Speed Forecasting

Yıl 2017, Cilt: 01 Sayı: 2, 46 - 53, 29.12.2017

Zeynep Bektaş Tarık Küçükdeniz Tuncay Özcan

Öz

Wind energy is one of the most promising resources of energy for the future. Wind is generally regarded as the most renewable and green energy type. The reason for this perception is mainly because of wind’s inexhaustible, sustainable and abundant characteristics. Recent years has witnessed a significant increase in wind energy investments. Wind speed forecasting is considered as the most important area of research with regard to better investment and planning decisions. In this study; support vector regression and multi-variable grey model with parameter optimization are applied to the wind speed forecasting problem. The main objective of this study is to reveal the possible usage and compare the performances of support vector regression against grey theory based forecasting. The performances of the selected algorithms are benchmarked on a sample dataset. The data was obtained from Cukurova region of Turkey. Experimental results indicate that multivariable grey model with parameter optimization outperforms support vector regression in terms of forecast accuracy. 

Anahtar Kelimeler

Support vector regression, Grey prediction, Wind speed forecasting

Kaynakça

• J. Jung and R. P. Broadwater, “Current status and future advances for wind speed and power forecasting”, Renewable and Sustainable Energy Reviews, vol. 31, (2014), pp. 762-777.
• L. Kamal and Y. Z. Jafri, “Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan”, Solar Energy, vol. 61, no. 1, (1997), pp. 23-32.
• M. C. Alexiadis, P. S. Dokopoulos, H. S. Sahsamanoglou and I. M. Manousaridis, “Short-term forecasting of wind speed and related electrical power”, Solar Energy, vol. 63, no. 1, (1998), pp. 61-68.
• H. Aksoy, Z. F. Toprak, A. Aytek and N. E. Ünal, “Stochastic generation of hourly mean wind speed data”, Renewable Energy, vol. 29, (2004), pp. 2111–2131.
• T. G. Barbounis and J. B. Theocharis, “Locally recurrent neural networks for wind speed prediction using spatial correlation”, Information Sciences, vol. 177, (2007), pp. 5775–5797.
• E. Cadenas and W. Rivera, “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model”, Renewable Energy, vol. 35, no. 12, (2010), pp. 2732–2738.
• D. S. Chen, P. Q. Li, X. R. Li, C. H. Xu, Y. Y. Xiao and B. Lei, “Short-term wind speed forecasting considering heteroscedasticity”, APAP 2011 - Proceedings: 2011 International Conference on Advanced Power System Automation and Protection, Beijing, China, (2011), October 16-20.
• Z. Hui, L. Bin and Z. Zhuo-qun, “Short term Wind Speed Forecasting Simulation Research Based on ARIMA LSSVM Combination Method” International Conference on Materials for Renewable Energy & Environment (ICMREE), Shanghai, China, (2011), May 20-22.
• E. Cadenas and W. Rivera, “Short term wind speed forecasting in La Venta, Oaxaca, Mexico, using artificial neural networks”, Renewable Energy, vol. 34 , no. 1, (2009), pp. 274–278.
• D. Liu, D. Niu, H. Wang and L. Fan, “Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm”, Renewable Energy: An International Journal, vol. 62, (2014), pp. 592–597.
• C. Ren, N. An, J. Wang, L. Li, B. Hu and D. Shang, “Knowledge-Based Systems Optimal parameters selection for BP neural network based on particle swarm optimization : A case study of wind speed forecasting”, Knowledge-Based Systems, vol. 56, (2014), pp. 226–239.
• H. Liu, H. Tian, X. Liang and Y. Li, “New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks”, Renewable Energy, vol. 83, (2015), pp. 1066–1075.
• Y. Ren, P. N. Suganthan and N. Srikanth, “A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods”, IEEE Transactions on Sustainable Energy, vol. 6, no. 1, (2015), pp. 236-244.
• O. B. Shukur and M. H. Lee, “Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA”, Renewable Energy, vol. 76, (2015), pp. 637–647.
• J. Wang, J. Hu, K. Ma and Y. Zhang, “Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China”, Renewable Energy, vol. 76, (2015), pp. 91–101.
• J. Wang, S. Qin, Q. Zhou and H. Jiang, “The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China”, Applied Energy, vol. 143, (2015a), pp. 472–488.
• F. Chengxian and L. Shuqin, “Wind speed forecasting method: Gray related weighted combination with revised parameter”, Energy Procedia, vol. 5, (2011), pp. 550–554.
• P. Jiang, Y. Wang and J. Wang, “Short-term wind speed forecasting using a hybrid model”, Energy, vol. 119, (2017), pp. 561–577.
• A. Lahouar and J. Ben Hadj Slama, “Hour-ahead wind power forecast based on random forests”, Renewable Energy, vol. 109, (2017), pp. 529–541.
• Z. Men, E. Yee, F. S. Lien, D. Wen and Y. Chen, “Short-term wind speed and power forecasting using an ensemble of mixture density neural networks”, Renewable Energy, vol. 87, (2016), pp. 203–211.
• J. Hu, J. Wang and L. Xiao, “A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts”, Renewable Energy, vol. 114, (2017), pp. 670–685.
• G. W. Chang, H. J. Lu, Y. R. Chang and Y. D. Lee, “An improved neural network-based approach for short-term wind speed and power forecast”, Renewable Energy, vol. 105, (2017), pp. 301–311.
• N. Chen, W. Lu, J. Yang and G. Li, “Support vector machine in chemistry” World Scientific Publishing, Singapore, (2004).
• C. Gao, E. Bompard, R. Napoli and H. Cheng, “Price forecast in the competitive electricity market by support vector machine”, Physica A: Statistical Mechanics and its Applications, vol. 382, no. 1, (2007), 98-113.
• D. Ju-Long, “Control problems of grey systems”, Systems and Control Letters, vol. 1, no. 5, (1982), pp.288-294.
• F. M. Tseng, H. C. Yu and G. H. Tzeng, “Applied hybrid grey model to forecast seasonal time series”, Technological Forecasting and Social Change, vol. 67, no. 2-3, (2001), pp. 291-302.
• L. C. Hsu, “Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models”, Expert Systems with Applications, vol. 36, (2009), pp. 7898-7903.