Artificial neural network analysis of Moroccan solar potential

Ahmed Ouammi, Driss Zejli, Hanane Dagdougui and Rachid Benchrifa

Renewable and Sustainable Energy Reviews, 2012, vol. 16, issue 7, 4876-4889

Abstract: An artificial neural network (ANN) model is used to forecast the annual and monthly solar irradiation in Morocco. Solar irradiation data are taken from the new Satellite Application Facility on Climate Monitoring (CM-SAF)-PVGIS database. The database represents a total of 12 years of data from 1998 to 2010. In this paper, the data are inferred using an ANN algorithm to establish a forward/reverse correspondence between the longitude, latitude, elevation and solar irradiation. Specifically, for the ANN model, a three-layered, back-propagation standard ANN classifier is considered consisting of three layers: input, hidden and output layer. The learning set consists of the normalised longitude, latitude, elevation and the normalised mean annual and monthly 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 the annual and monthly maps. They indicate that the method could be used by researchers or engineers to provide helpful information for decision makers in terms of sites selection, design and planning of new solar plants.

Keywords: Solar energy potential; Artificial neural network; Geographic information system; Morocco (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (23)

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DOI: 10.1016/j.rser.2012.03.071

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