Photovoltaic output power forecast using artificial neural networks

Abderrazzak Elamim (1) , Bouchaib Hartiti (1) , A. Barhdadi (2) , Amine Haibaoui (3) , Abderrazak Lfakir (4) , Philippe Thevenin (5)

1 ERDyS - ERDYS laboratory [Hassan II University of Casablanca]
2 ENS Rabat - École normale supérieure - Rabat
3 LIMAT - Laboratoire d'Ingénierie et Matériaux [Casablanca]
4 USMS - Université Sultan Moulay Slimane
5 LMOPS - Laboratoire Matériaux Optiques, Photonique et Systèmes

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.

Keywords: Artificial neural networks; Feed forward neural network; Photovoltaic installation

Domains: Engineering Sciences [physics]

Abderrazzak Elamim, Bouchaib Hartiti, A. Barhdadi, Amine Haibaoui, Abderrazak Lfakir, et al.. Photovoltaic output power forecast using artificial neural networks. Journal of Theoretical and Applied Information Technology, 2018, 96 (15), pp.5116-5126. ⟨hal-02434084⟩