New SVM kernel soft computing models for wind speed prediction in renewable energy applications

This paper proposes a hybrid multi-step wind speed prediction model based on combination of singular spectrum analysis (SSA), variational mode decomposition (VMD) and support vector machine (SVM) and was applied for sustainable renewable energy application. In the proposed SSA–VMD–SVM model, the SSA was applied to eliminate the noise and to approximate the signal with trend information; VMD was applied to decompose and to extract the features of input time series wind speed data into a number of sub-layers; and the SVM model with various kernel functions was adopted to predict the wind speed from each of the sub-layers, and the parameters of SVM were fine-tuned by differential evolutionary algorithm. To investigate the effectiveness of the proposed model, various prediction models are considered for comparative study, and it is demonstrated that the proposed model outperforms with better prediction accuracy.

[1]  Tarlochan Kaur,et al.  Application of artificial neural network for short term wind speed forecasting , 2016, 2016 Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy (PESTSE).

[2]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[3]  Y. Y. Chen,et al.  A hybrid model for forecasting wind speed and wind power generation , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[4]  Ponnuthurai N. Suganthan,et al.  A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Xue Yusheng,et al.  Ultra-short-term wind speed forecasting method based on spatial and temporal correlation models , 2017 .

[6]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[7]  Zhang Yuxian,et al.  Performance comparison of ANNs model with VMD for short‐term wind speed forecasting , 2018, IET Renewable Power Generation.

[8]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[9]  Jian Zhang,et al.  A Novel Short-Term Wind Speed Prediction Based on MFEC , 2016, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[10]  Xiong Luo,et al.  Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy , 2018, IEEE Transactions on Industrial Informatics.

[11]  Carolina M. Affonso,et al.  Hybrid Approach Combining SARIMA and Neural Networks for Multi-Step Ahead Wind Speed Forecasting in Brazil , 2018, IEEE Access.

[12]  Yuan Zhao,et al.  Wind Speed Prediction of IPSO-BP Neural Network Based on Lorenz Disturbance , 2018, IEEE Access.

[13]  Men-Tzung Lo,et al.  Empirical mode decomposition based detrended sample entropy in electroencephalography for Alzheimer's disease , 2012, Journal of Neuroscience Methods.

[14]  Zahid Ullah,et al.  Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for electrical load and wind speed , 2018, The Journal of Engineering.

[15]  P. N. Suganthan,et al.  A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods , 2015, IEEE Transactions on Sustainable Energy.

[16]  Song Xing,et al.  Short-term wind speed forecasting using wavelet transformation and AdaBoosting neural networks in Yunnan wind farm , 2017 .

[17]  Steven M. Pincus.  Assessing Serial Irregularity and Its Implications for Health , 2001, Annals of the New York Academy of Sciences.

[18]  Ankita Singh,et al.  Short term wind speed and power forecasting in Indian and UK wind power farms , 2016, 2016 IEEE 7th Power India International Conference (PIICON).

[19]  Venkata Dinavahi,et al.  Wavelet Neural Network Based Multiobjective Interval Prediction for Short-Term Wind Speed , 2018, IEEE Access.

[20]  Ercan E. Kuruoglu,et al.  One-day ahead wind speed/power prediction based on polynomial autoregressive model , 2017 .

[21]  Bo-Suk Yang,et al.  Intelligent prognostics for battery health monitoring based on sample entropy , 2011, Expert Syst. Appl..

[22]  Okyay Kaynak,et al.  Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting , 2017, IEEE Transactions on Industrial Informatics.

[23]  Qinghua Hu,et al.  Short-Term Wind Speed or Power Forecasting With Heteroscedastic Support Vector Regression , 2016, IEEE Transactions on Sustainable Energy.

[24]  Antonio Vicino,et al.  Bidding Wind Energy Exploiting Wind Speed Forecasts , 2016, IEEE Transactions on Power Systems.

[25]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.