Short-term wind speed forecasting based on the Jaya-SVM model

Abstract

Wind energy is an emerging environmentally friendly energy source. However, due to the uncertainty and volatility of wind speed, wind energy cannot be effectively exploited, and it is essential to build an accurate wind speed forecasting model. In this paper, a Jaya algorithm-based support vector machine (Jaya-SVM) model is proposed for short-term wind speed forecasting. Different from the typical SVM regression, the most representative features of input data are selected and the hyper-parameters of SVM are optimized by using the Jaya optimization algorithm. To examine the performance of the Jaya-SVM model, seven other wind speed forecasting models, Least Absolute Shrinkage and Selection Operator, Extreme Gradient Boosting model, Multi-Layer Perceptron Regression model, Deep Belief Network, Gaussian Process Regression, Stacked Sparse Autoencoder and Granular Computing method are employed for comparison. The wind speed data collected in Jilin, China is fully utilized. The computational results demonstrate that the Jaya-SVM model generates the best results among all eight models in terms of MAE, MSE, MAPE, R2 and reliability, having the capacity of accurate wind speed forecasting.