Performance Analysis of Wind Power Prediction using ANFIS and Gaussian-SVM


  • Devyani Patidar DDIPG Scholar, Department of Electrical & Electronics Engineering, UTD RGPV, Bhopal, M.P, India
  • Dr. Krishna Teerth Chaturvedi Professor, Department of Electrical & Electronics Engineering, UTD RGPV, Bhopal, M.P,India



Wind energy is one of the most economical sources of renewable electricity, with the largest resources available in the world. It is one of the most promising sources of clean energy and extends its reach to electricity production. Today, wind technologies are making a significant contribution to the growing clean electricity market worldwide. The rapid growth of wind energy and the increase in wind energy production require serious research in various fields. Because wind energy depends on time, it is variable and intermittent on different time scales. Therefore, accurate wind energy prediction is considered an important contribution to the reliable integration of large scale wind energy. Wind energy forecasting methods can be used to plan the ownership, planning and delivery of activities by network operators to maximize electricity traders' revenues. The increasing prevalence of wind power in power plants raises important questions arising from their intermittent and uncertain nature. These challenges require a precise prediction tool for wind power generation to plan the efficient operation of electrical systems and ensure reliability of supply. In this research work two classifier’s performance are evaluated on the real-time dataset. The accuracy of the models has been measured using four performance metrics namely, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error(MAPE). From the result analysis it has been concluded that gaussian SVM outperforms better as compared to the ANFIS model. The result is analysed on different testing dataset of different seasons and averaged error is used to analyse the performance measures.


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J. P. S. Catalão, G. J. Osório, H. M. I. Pousinho, “Short– term wind power forecasting using a hybrid evolutionary intelligent approach”, 16th Int. Conf. Int. Syst. Appl. to Power Syst., pp. 1-5, 2011.

M. Khalid, A. V. Savkin. “A method for short-term wind power prediction with multiple observation points”, IEEE Trans. Power Syst., Volume 27, pp. 579-586, 2012.

G. Sideratos, N. Hatziargyriou. “Wind power forecasting focused on extreme power system events”, IEEE Trans. Sust. Energy, Volume 3, pp. 445-454, 2012.

G. K. Venayagamoorthy, K. Rohrig, I. Erlich. “Short-term wind power forecasting and intelligent predictive control based on data analytics”, IEEE Power Ener. Mag., Volume 10, pp. 71-78, 2012.

G. Sideratos, N. Hatziargyriou. “Probabilistic wind power forecasting using radial basis function neural network”, IEEE Trans. Pow. Syst., Volume 27, pp. 1788-1796, 2012.

Y. Liu, J. Shi, Y. Yang, W.-J. Lee. “Shot-term wind-power prediction based on wavelet transform-support vector machine and statistic-characteristics analysis”, IEEE Trans. Indus. Appl., Volume 48, pp. 1136-1141, 2012.

K. Bhaskar, S. N. Singh. “AWNN-Assisted wind power forecasting using feed-forward neural network”, IEEE Trans. Sust. Ener., Volume 3, pp. 306-315, 2012.

Tiago Pinto, Sérgio Ramos, Tiago M. Sousa, Zita Vale, “Short-term Wind Speed Forecasting using Support Vector Machines”, IEEE, 2014.

Yordanos Kassa, J. H. Zhang, D. H. Zheng, Dan Wei, “Short Term Wind Power Prediction Using ANFIS”, IEEE International Conference on Power and Renewable Energy, 2016.

Gang Zhang “Prediction of Short-Term Wind Power in Wind Power Plant based on BP-ANN”,IEEE, 2016

Anwen Zhu, Xiaohui Li, Zhiyong Mo, Huaren Wu “Wind Power Prediction Based on a Convolution Neural Network” IEEE, 2017.

Krishnaveny R. Nair “Forecasting of wind speed using ANN, ARIMA and Hybrid Models” International Conference on Intelligent Computing Instrumentation and Control Technologies, 2017.

Runhai Jiaol “A Model Combining Stacked Auto Encoder and Back Propagation Algorithm for Short-term Wind Power Forecasting” , IEEE, 2018.




How to Cite

Patidar, D., & Chaturvedi, D. K. T. (2018). Performance Analysis of Wind Power Prediction using ANFIS and Gaussian-SVM. SMART MOVES JOURNAL IJOSCIENCE, 4(5), 1–7.