Analysis of support vector regression kernels for energy storage efficiency prediction

Renewable energy systems (RES) penetration has improved interest in energy storage systems. This has helped to increase RES acceptance because their reliability has improved. Currently, scholars have recognized compressed air energy storage (CAES) systems as efficient storage systems for RES, but there is a need to subject the implications of these systems’ parametric settings on their storage efficiency. Hence, this study proposes and selects a suitable support vector regression model for storage efficiency prediction. It considered five input parameters — among which are Maximum exit temperature and thermal energy. Data sets for CAES with high temperatures (HTE) were used to analyze the performance of different kernels – radial basis function (RBF), polynomial, and linear – for SVR models. These data were divided into train and test examples. The study observed that an RBF trained-SVR model can accurately predict the storage efficiency of CAES-THE, whereas a polynomial trained-SVR cannot predict the storage efficiency of CAES-HTE. The RBF trained-SVR model testing examples correlation coefficient was higher than its training examples result (0.9855) by 1.43%.