Support Vector Forecasting of Solar Radiation Values
The increasing importance of solar energy has made the accurate forecasting of radiation an important issue. In this work we apply Support Vector Regression to downscale and improve 3-hour accumulated radiation forecasts for two locations in Spain. We use either direct 3-hour SVR-refined forecasts or we build first global accumulated daily predictions and disaggregate them into 3-hour values, with both approaches outperforming the base forecasts. We also interpolate the 3-hour forecasts into hourly values using a clear sky radiation model. Here again the disaggregated SVR forecast perform better than the base ones, but the SVR advantage is now less marked. This may be because of the clear sky assumption made for interpolation not being adequate for cloudy days or because of the underlying clear sky model not being adequate enough. In any case, our study shows that machine learning methods or, more generally, hybrid artificial intelligence systems are quite relevant for solar energy prediction.
KeywordsSolar energy radiation support vector regression
Unable to display preview. Download preview PDF.
- 1.Bird, R.E., Hulstrom, R.: Simplified clear sky model for direct and diffuse insolation on horizontal surfaces. Tech. Rep. No. SERI/TR-642-761, Solar Energy Research Institute, Golden, CO (1981)Google Scholar
- 2.Bofinger, S., Heilscher, G.: Solar electricity forecast–approaches and first results. In: 21st European Photovoltaic Solar Energy Conference (2006)Google Scholar
- 4.Guarnieri, R., Martins, F., Pereira, E., Chuo, S.: Solar radiation forecasting using artificial neural networks. National Institute for Space Research 1, 1–34 (2008)Google Scholar
- 5.Jensenius, J., Cotton, G.: The development and testing of automated solar energy forecasts based on the model output statistics (MOS) technique. In: 1st Workshop on Terrestrial Solar Resource Forecasting and on Use of Satellites for Terrestrial Solar Resource Assessment, pp. 22–29 (1981)Google Scholar
- 8.Lorenz, E., Remund, J., Müller, S., Traunmüller, W., Steinmaurer, G., Ruiz-Arias, J., Fanego, V., Ramirez, L., Romeo, M., Kurz, C., Pomares, L., Guerrero, C.: Benchmarking of different approaches to forecast solar irradiance. In: 24th European Photovoltaic Solar Energy Conference, pp. 4199–4208 (2009)Google Scholar
- 10.Meinel, A.B., Meinel, M.P.: Applied Solar Energy. Addison Wesley Publishing Co. (1976)Google Scholar
- 14.Remund, J., Perez, R., Lorenz, E.: Comparision of solar radiation forecasts for the USA. In: European PV Conference, Valencia, Spain (2008)Google Scholar
- 15.Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2001)Google Scholar