NDLI: Artificial Neural Network Analysis of Moroccan Solar and Wind Potential and their Use in Hydrogen Production

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Artificial Neural Network Analysis of Moroccan Solar and Wind Potential and their Use in Hydrogen Production

Content Provider	Semantic Scholar
Author	Dagdougui, Younès Berrhazi, Samir Zejli, Driss Benchrifa, Rachid
Copyright Year	2014
Abstract	An artificial neural network (ANN) model is used to forecast annual wind speeds and solar irradiation and to predict the annual hydrogen potential in Morocco. Solar irradiation data are taken from the new Satellite Application Facility on Climate Monitoring (CM-SAF) - PVGIS database. The annual wind speed data are taken from (CDER, 2007). In this paper, the data are inferred using an ANN algorithm to establish a forward/reverse correspondence between the longitude, latitude, elevation, solar irradiation and wind speed. The paper aims to present a useful tool for the selection of locations with high hydrogen production, based mainly on the use of solar and wind resources. Specifically, for the ANN model, a three-layered, back-propagation standard ANN classifier is considered consisting of three layers: input, hidden and output layers. The learning set consists of the normalized longitude, latitude, elevation and the normalized mean annual wind speed of 20 sites and the normalized mean annual solar irradiation of 41 Moroccan sites. The testing set consists of patterns just represented by the input component, while the output component is left unknown and its value results from the ANN algorithm for that specific input. The results are given in the form of annual wind speed, solar irradiation and anual hydrogen potential maps. It indicates that the method could be used by researchers or engineers to provide helpful information for decision makers in terms of site selection, design and planning of new solar and/or wind power plants as well as future hydrogen infrastructures.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.isesco.org.ma/ISESCO_Technology_Vision/NUM18/doc/1.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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