caa a22 4500 606159967 CHVBK 20210128100627.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00521-015-1842-y doi (NATIONALLICENCE)springer-10.1007/s00521-015-1842-y Photovoltaic energy production forecast using support vector regression [Elektronische Daten] [R. De Leone, M. Pietrini, A. Giovannelli] Forecasting models for photovoltaic energy production are important tools for managing energy flows. The aim of this study was to accurately predict the energy production of a PV plant in Italy, using a methodology based on support vector machines . The model uses historical data of solar irradiance, environmental temperature and past energy production to predict the PV energy production for the next day with an interval of 15min. The technique used is based on $$\nu $$ ν -SVR, a support vector regression model where you can choose the number of support vectors. The forecasts of energy production obtained with the proposed methodology are very accurate, with the $$R^{2}$$ R 2 coefficient exceeding 90% . The quality of the predicted values strongly depends on the goodness of the weather forecast, and the $$R^{2}$$ R 2 value decreases if the predictions of irradiance and temperature are not very accurate. The Natural Computing Applications Forum, 2015 Forecasting model nationallicence Support vector machines nationallicence PV energy production nationallicence De Leone R. School of Science and Technologies, University of Camerino, Camerino, MC, Italy aut Pietrini M. School of Science and Technologies, University of Camerino, Camerino, MC, Italy aut Giovannelli A. Research@energy, Loccioni Group, Angeli di Rosora, AN, Italy aut Neural Computing and Applications Springer London 26/8(2015-11-01), 1955-1962 0941-0643 26:8<1955 2015 26 521 https://doi.org/10.1007/s00521-015-1842-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-015-1842-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- De Leone R. School of Science and Technologies, University of Camerino, Camerino, MC, Italy aut NATIONALLICENCE 700 1- Pietrini M. School of Science and Technologies, University of Camerino, Camerino, MC, Italy aut NATIONALLICENCE 700 1- Giovannelli A. Research@energy, Loccioni Group, Angeli di Rosora, AN, Italy aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/8(2015-11-01), 1955-1962 0941-0643 26:8<1955 2015 26 521