Support Vector Regression of Multiple Predictive Models of Downward Short-Wave Radiation

0429748 - ÚI 2015 RIV US eng C - Conference Paper (international conference)
Krömer, P. - Musílek, P. - Pelikán, Emil - Krč, Pavel - Juruš, Pavel - Eben, Kryštof
Support Vector Regression of Multiple Predictive Models of Downward Short-Wave Radiation.
Proceedings of the 2014 International Joint Conference on Neural Networks. Piscataway: IEEE Computer Society, 2014, s. 651-657. ISBN 978-1-4799-1484-5.
[IJCNN 2014. International Joint Conference on Neural Networks. Beijing (CN), 06.07.2014-11.07.2014]
R&D Projects: GA MŠMT LD12009
Grant - others:SGS VŠB(CZ) SP2014/110
Institutional support: RVO:67985807
Keywords : renewable energies * multimodel forecasting * combining prediction
Subject RIV: IN - Informatics, Computer Science

Accurate forecasts of weather conditions are of the utmost importance for the management and operation of renewable energy sources with intermittent (stochastic) production. With the growing amount of intermittent energy sources, the need for precise weather predictions increases. Production of energy from renewable power sources, such as wind and solar, can be predicted using numerical weather prediction models. These models can provide high-resolution, localized forecast of wind speed and solar irradiation. However, different instances of numerical weather prediction models may provide different forecasts, depending on their properties and parameterizations. To alleviate this problem, it is possible to employ multiple models and to combine their outputs to obtain more accurate localized forecasts. This work uses the machine-learning tool of Support Vector Regression to amalgamate downward short-wave radiation forecasts of several numerical weather prediction models. Results of SVR-based multi-model forecasts of irradiation at a large set of locations show a significant improvement of prediction accuracy.
Permanent Link: http://hdl.handle.net/11104/0235404