Short-term PV power forecasting using Support Vector Regression and local monitoring data
| Autor: | Mohamed Tabaa, Lhoussine Bahatti, Brahim Chouri, Abderrahmane Jarrou, Ayoub Fentis, Mohamed Mestari |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
Coefficient of determination Mean squared error business.industry 020209 energy Photovoltaic system Weather forecasting 02 engineering and technology computer.software_genre Data modeling Term (time) Support vector machine 0202 electrical engineering electronic engineering information engineering Econometrics Performance indicator business computer |
| Zdroj: | 2016 International Renewable and Sustainable Energy Conference (IRSEC). |
| DOI: | 10.1109/irsec.2016.7983968 |
| Popis: | In recent years many research works have study the problem of photovoltaic power forecasting because of its importance to grid management and large-scale PV integration. In order to forecast the Photovoltaic power production in the region of Casablanca Morocco, a simple and reliable model based on Support Vector Regression (SVR) and local monitoring data is proposed in this paper. Three models based on e-SVR, ν-SVR and LS-SVR are compared using five performance indicators, MAE, MSE, RMSE, R2 and RRMSE (%). The best model shows a good results with an RRMSE of 15.23% and a coefficient of determination R2 = 0.96%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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