| Título: | Machine Learning Prediction of Global Photovoltaic Energy in Spain. ICREPQ’14, International Conference on Renewable Energies and Power Quality. Córdoba, España, Abr. 8-11, 2014. Proceedings. Paper 5, 423. Renewable Energy and Power Quality Journal (RE&PQJ). ISSN 2172-038 X, No.12, April 2014. |
|---|---|
| Autor: | Gala, Y., Fernández, A., Dorronsoro, J. R., García, M., & Rodríguez, C. |
| Año: | 2014 |
| Enlace: | http://icrepq.com/icrepq’14/423.14-Gala.pdf |
Abstract
The growing presence of solar energy in the electrical systems of many countries has made its accurate forecasting an important issue. In this work we will explore the application of Support Vector Regression (SVR), an advanced Machine Learning modeling tool, to forecast the daily photovoltaic generation of Spain. Given the very large geographical spread of photovoltaic installations, we will use as input features NWP forecasts of relevant meteorological variables for the entire Iberian Peninsula.
The input dimension is thus very large but, while further work is needed, our results show SVR to be an effective tool to deal with the problem’s underlying dimension, yield useful forecasts and further provide some insights on the relationship between NWP and actual solar energy production.