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Photovoltaic output power forecast using artificial neural networks

Domain:

environment and energy

Record type:

paper
Creator:
ElaHarBarHai
Editor:
ERDÉcoLabUni
Publisher:
CCSDJAT
Host:avatar
International audience This article presents a method for predicting the power provided by photovoltaic solar panels using feed forward neural network (FFNN) of a photovoltaic installation located in the city of Mohammedia (Morocco). An almost one-year experimental database on solar irradiance, ambient temperature and PV power were used to study the prediction ability of the power produced by artificial neural networks. To verify this model, the coefficient of determination (R2), the normalized mean squared error (nRMSE), the mean absolute error (MAE), and other parameters were used. The results of this model tested on unknown data showed that the model works well, with determination coefficients lying between 0.99 and 0.998 for sunny days, between 0.961 and 0.965 for cloudy days and between 0.88 and 0.93 for rainy days. © 2005 – ongoing JATIT and LLS.

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centralesupelec.hal.science

Tags

Photovoltaic installationFeed forward neural networkArtificial neural networks[SPI]Engineering Sciences [physics]

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