Abstract

Comparison of kernel functions of support vector machines: A case study for the solar cell output power prediction

Abstract

Four kernel functions of support vector machines (SVM), namely, radial basis function, sigmoid function, linear function and polynomial function, were applied for the prediction of solar cell output power. Two types of SVM model such as epsilon-SRV and nu-SVR were chosen for each kernel function. Measured values of temperature T (°C) and irradiance E (〖kWh.m〗^(-2)) were used as inputs and solar cell output power P (kW) was used as output. The accuracy of each kernel function was evaluated using well known statistical parameters. Radial basis function using nu-SVR and polynomial function using epsilon-SVR provided similar and better results than other kernels. However, polynomial function has taken more analysis run time while radial basis function used more number of support vectors than other kernels. They may be more computationally expensive.

Keywords

• Kernel function,PV output power,SVM

References

1. R., BoroumandJazi, G., Mekhlif, S., Jameel, M., 2012, “Exergy analysis of solar energy applications”, Renewable and Sustainable Energy Reviews, 16(1)), 350-356.
2. Sopori, B., 2002, ”Silicon Solar-Cell Processing for Minimizing the Influence of Impurities and Defects”, Journal of Electronic Materials, 31,972-980.
3. Hossain, R., Maung, A., Than O,. Shawkat, A., 2013,”Hybrid Prediction Method for Solar Power Using Different Computational Intelligence Algorithms”,Smart Grid and Renewable Energy, 4,76-87.
4. Soteris, A., Kalogirou Şencan, A., 2010, “Artificial Intelligence Techniques in Solar Energy Applications”, www.intechopen, 315-340.
5. Hussein, A., Kazem, Jabar, H., Yousif, Miqdam T Chaichan, 2016, ”Modelling of Daily Solar Energy System Prediction using Support Vector Machine for Oman”, International Journal of Applied Engineering Research,11(20), 10166-10172.
6. Anuwar, F., Omar, A., 2016,” Future Solar Irradiance Prediction using Least Square Support Vector Machine”, International Journal on Advanced Science Engineering Information Technology, 6(4), 513-520.
7. Yekkehkhany, B., Safari, A., Homayouni S., Hasanlou, M., 2014,” A Comparison Study of Different Kernel Functions for SVM-based Classification of Multi-temporal Polarimetry SAR Data”, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-2/W3, The 1st ISPRS International Conference on Geospatial Information Research, 15–17, Tehran, Iran.
8. Supriya P., Deepak S., 2015, ”Comparison of Various Kernels of Support Vector Machine”, International Journal for Research in Applied Science & Engineering Technology, 3 (7),.532-536.

9. Baudat, G., Anouar, F., 2003, “Feature vector selection and projection using kernels”, Neurocomputing, 55(1-2), 21-38.
10. Hong, Z., Haibin, L., Xingjian, L., Tong R., 2018,” A Multiple Kernel Learning Approach for Air Quality Prediction”, ID 3506394.
11. Yin,W., Cho,J., Kai,W., Michael, R., Chih,J., 2010, Training and Testing Low-degree Polynomial Data Mappings via Linear SVM,11, 1471-1490.
12. Corinna, C., Vladimir, V., 1995, “Support-Vector Networks, Machine Learning”, 20, 273-297.
13. Shawe, J.; Cristianini, N., 2004,” Kernel Methods for Pattern Analysis”, Cambridge University Press.
14. Thomas, H., Bernhard, S., Alexande, R., Smola, J., 2008,” Kernel Methods in Machine Learning”, The Annals of Statistics, 36(3), 1171–1220.
15. Rob J., Koehler B., 2006 “Another look at measures of forecast accuracy”, International Journal of Forecasting, 22(4), 679-688.
16. Willmott C., Matsuura, K., 2006, ”On the use of dimensioned measures of error to evaluate the performance of spatial interpolators”, International Journal of Geographical Information Science, 20(1), 89-102.
17. Phillip H. S., 2014, DTREG, “Predictive Model Software”.
18. Alaa T., 2019, “Parameter investigation of support vector machine classifier with kernel functions”, Knowledge and Information Systems.