Comparative study of nonparametric and parametric PV models to forecast AC power output of PV plants
| Autor: | Almeida, Marcelo Pinho, Muñoz Escribano, Mikel, Parra Laita, Íñigo de la, Perpiñán, Óscar, Narvarte Fernández, Luis |
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| Přispěvatelé: | Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica y Electrónica, Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektriko eta Elektronikoa Saila, Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa. ISC - Institute of Smart Cities |
| Rok vydání: | 2015 |
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| Zdroj: | Academica-e. Repositorio Institucional de la Universidad Pública de Navarra instname Academica-e: Repositorio Institucional de la Universidad Pública de Navarra Universidad Pública de Navarra EU PVSEC Proceedings | 31st European Photovoltaic Solar Energy Conference and Exhibition: EUPVSEC 2015 | 14/09/2015-18/09/2015 | Hamburg, Germany Archivo Digital UPM |
| Popis: | Trabajo presentado a la 31st European Photovoltaic Solar Energy Conference and Exhibition (EU PVSEC). Hamburgo, 2015. Incluye póster In this paper, a comparison between two approaches to predict the AC power output of PV systems is carried out in terms of forecast performance. Each approach uses one of the two main types of PV modeling, parametric and nonparametric, and both use as inputs several forecasts of meteorological variables from a Numerical Weather Prediction model. Furthermore, actual AC power measurements of a PV plant are used to train the nonparametric model, to adjust the parameters of the different PV components models used in the parametric approach and to assess the quality of the forecasts. The approaches presented similar behavior, although the nonparametric approach, based on Quantile Regression Forests, showed smaller biased errors due to the machine learning tool used. This work has been partially financed by the Seventh Framework Programme of the European Commission with the Project Photovoltaic Cost Reduction, Reliability, Operational Performance, Prediction and Simulation (PVCROPS—Grant Agreement No. 308468). |
| Databáze: | OpenAIRE |
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