Support Vector Regression Approach for Wind Forecasting

Mohamad Yamin (1), Ahmad Fakhri Giyats (2)
(1) Department of Mechanical Engineering, Faculty of Industrial Technology, Gunadarma University, Depok 16424, Indonesia
(2) Department of Mechanical Engineering, Faculty of Industrial Technology, Gunadarma University, Depok 16424, Indonesia
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How to cite (IJASEIT) :
Yamin, M., & Giyats, A. F. (2022). Support Vector Regression Approach for Wind Forecasting. International Journal of Advanced Science Computing and Engineering, 4(2), 95–101. https://doi.org/10.62527/ijasce.4.2.84

The government policies to fully support the G20 sustainability emphasize increased use of renewable energy. The high penetration of wind energy into power systems poses many challenges for energy system operators, primarily due to the unpredictability and variability of wind energy production. Wind power may not be provided, but accurate forecasting of wind speed and power generation helps grid operators reduce the risk of reduced electricity reliability. Accurately predicting wind speeds over 1 to 24 hours based on these conditions is important for predicting potential energy supply. These short-term forecasts are important to support wind power planning, so the required base load supply for the grid is always guaranteed (even if the wind power output fluctuates significantly). This task demonstrates that the relative forecasting performance of a support vector regression (SVR) wind forecasting system can be improved by systematically selecting and combining related input functions that affect wind speed. Shows the results of data collected in Sidrap, Indonesia, during the six months of 2019. This paper explained key methods of wind forecasting, based on the evaluation of wind speeds and wind speed prediction methods. The RMSE from the SVR shows an 8% - 9% improvement on the RMSE of the persistence forecast every 1 hour. Wind speed estimation using a support vector regression approach has the potential for further development, one of which is determining the potential location of wind-based renewable sources and Wind Energy Conversion System (WECS) can make more efficient.

N. Botha dan C. M. van der Walt, “Forecasting wind speed using support vector regression and feature selection,†dalam 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech), Bloemfontein, South Africa, Nov 2017, hlm. 181–186. doi: 10.1109/RoboMech.2017.8261144.

A. Banik, C. Behera, Tirunagaru. V. Sarathkumar, dan A. K. Goswami, “Uncertain wind power forecasting using LSTMâ€based prediction interval,†IET Renew. Power Gener., vol. 14, no. 14, hlm. 2657–2667, Okt 2020, doi: 10.1049/iet-rpg.2019.1238.

T. Niu, J. Wang, K. Zhang, dan P. Du, “Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy,†Renew. Energy, vol. 118, hlm. 213–229, Apr 2018, doi: 10.1016/j.renene.2017.10.075.

C. Yang, X. Yang, dan X. Xiao, “Data-driven projection method in fluid simulation: Data-driven projection method in fluid simulation,†Comput. Animat. Virtual Worlds, vol. 27, no. 3–4, hlm. 415–424, Mei 2016, doi: 10.1002/cav.1695.

M. H. Do dan D. Söffker, “State-of-the-art in integrated prognostics and health management control for utility-scale wind turbines,†Renew. Sustain. Energy Rev., vol. 145, hlm. 111102, Jul 2021, doi: 10.1016/j.rser.2021.111102.

V. Nikolić, S. Sajjadi, D. Petković, S. Shamshirband, Ž. Ćojbašić, dan L. Y. Por, “Design and state of art of innovative wind turbine systems,†Renew. Sustain. Energy Rev., vol. 61, hlm. 258–265, Agu 2016, doi: 10.1016/j.rser.2016.03.052.

Z. Peng dkk., “A novel deep learning ensemble model with data denoising for short-term wind speed forecasting,†Energy Convers. Manag., vol. 207, hlm. 112524, Mar 2020, doi: 10.1016/j.enconman.2020.112524.

S. R. Moreno, V. C. Mariani, dan L. dos S. Coelho, “Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast,†Renew. Energy, vol. 164, hlm. 1508–1526, Feb 2021, doi: 10.1016/j.renene.2020.10.126.

W. Fu, K. Wang, J. Tan, dan K. Zhang, “A composite framework coupling multiple feature selection, compound prediction models and novel hybrid swarm optimizer-based synchronization optimization strategy for multi-step ahead short-term wind speed forecasting,†Energy Convers. Manag., vol. 205, hlm. 112461, Feb 2020, doi: 10.1016/j.enconman.2019.112461.

Ll. Lledó, V. Torralba, A. Soret, J. Ramon, dan F. J. Doblas-Reyes, “Seasonal forecasts of wind power generation,†Renew. Energy, vol. 143, hlm. 91–100, Des 2019, doi: 10.1016/j.renene.2019.04.135.

E. C. Malz, V. Verendel, dan S. Gros, “Computing the power profiles for an Airborne Wind Energy system based on large-scale wind data,†Renew. Energy, vol. 162, hlm. 766–778, Des 2020, doi: 10.1016/j.renene.2020.06.056.

N. E. Huang dkk., “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis,†Proc. R. Soc. Lond. Ser. Math. Phys. Eng. Sci., vol. 454, no. 1971, hlm. 903–995, Mar 1998, doi: 10.1098/rspa.1998.0193.

S. Zhang, C. Liu, W. Wang, dan B. Chang, “Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction,†Entropy, vol. 22, no. 10, hlm. 1102, Sep 2020, doi: 10.3390/e22101102.

Y. Ren, P. N. Suganthan, dan N. Srikanth, “A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting,†IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 8, hlm. 1793–1798, Agu 2016, doi: 10.1109/TNNLS.2014.2351391.

L. Lisapaly, “An academic review on the performance of the Sidrap wind turbine, Sulawesi – Indonesia,†IOP Conf. Ser. Earth Environ. Sci., vol. 878, no. 1, hlm. 012058, Okt 2021, doi: 10.1088/1755-1315/878/1/012058.

G. Santamaría-Bonfil, A. Reyes-Ballesteros, dan C. Gershenson, “Wind speed forecasting for wind farms: A method based on support vector regression,†Renew. Energy, vol. 85, hlm. 790–809, Jan 2016, doi: 10.1016/j.renene.2015.07.004.