@conference{BillelCongreso1,
author = "Billel Amiri and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and Rabah Diz\'ene and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Dahmani Kahina",
abstract = "This work applies evolutionary product unit neural networks (EPUNNs) to estimate global inclined irradiation at real time and predict it 10 minutes in advance. Both tasks are accomplished simultaneously, by using one single model with two outputs. One advantage of our approach is that the predictions of inclined irradiation are obtained without the need of a series of historical data. In this way, the model only considers one measured input variable, which is the horizontal global irradiation at the previous instant. Besides, the evolutionary algorithm used to optimize the network allows us to obtain the best adapted topology of the model with respect to the number of hidden neurons and synaptic connections. Very promising results are obtained, where the inclined irradiation I β (t) is estimated with an accuracy of 5.10% of nRMSE, while it is predicted 10 minutes in advance with an accuracy of 16.97%.",
booktitle = "Proceedings of the 2019 International Conference of Computer Science and Renewable Energies (ICCSRE)",
doi = "10.1109/ICCSRE.2019.8807613",
isbn = "978-1-7281-0827-8",
keywords = "solar irradiation, tilted plane, forecasting, artificial neural network, evolutionary learning, product units, evolutionary programming",
month = "22th-24th July",
organization = "Agadir, Morocco",
publisher = "IEEE Press",
title = "{T}en {M}inutes {S}olar {I}rradiation {F}orecasting on {I}nclined {P}lane using {E}volutionary {P}roduct {U}nit {N}eural {N}etworks",
url = "doi.org/10.1109/ICCSRE.2019.8807613",
year = "2019",
}