Konferensartikel

Machine Learning Approach for Next Day Energy Production Forecasting in Grid Connected Photovoltaic Plants

L. Mora-López
Dpto. Lenguajes y Ciencias de la Comunicación. Universidad de Málaga. Málaga, Spain

I. Martínez-Marchena
Dpto. Lenguajes y Ciencias de la Comunicación. Universidad de Málaga. Málaga, Spain

M. Piliougine
Dpto. Física Aplicada II. Universidad de Málaga, Málaga, Spain

M. Sidrach-deCardona
Dpto. Física Aplicada II. Universidad de Málaga, Málaga, Spain

Ladda ner artikelhttp://dx.doi.org/10.3384/ecp110572869

Ingår i: World Renewable Energy Congress - Sweden; 8-13 May; 2011; Linköping; Sweden

Linköping Electronic Conference Proceedings 57:24, s. 2869-2874

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Publicerad: 2011-11-03

ISBN: 978-91-7393-070-3

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

This paper presents a model for predicting the next-day energy production of a photovoltaic solar plant. The model is capable of forecasting the next-day production profile of such a system; merely by using the information obtained from the plant itself and the solar global radiation values for the previous operation days. This prediction is key in many photovoltaic systems in order to interact with conventional electrical grids. For example; Spanish legislation requires this type of information for large photovoltaic plants. In fact; the deviations from the predicted values are financially penalized. A three-stage procedure is used to build the model; which is capable of learning specific information about each facility and of using this information to fit the prediction. This model binds the use of regression techniques and the use of a special type of probabilistic finite automata developed from machine learning. The energy prediction yearly error is less that 20 percent which is a significant improvement over previous proposed models; whose errors are around 25 percent.

Nyckelord

Short term forecasting; photovoltaic energy production; machine learning

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