Analysis of moroccan wind and solar potential using artificial neural network approach

Abstract

An artificial neural network (ANN) model is used to forecast the annual wind speeds and solar irradiation 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. Specifically, for the ANN model, a three-layered, backpropagation 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 wind speed of 20 sites and the normalised 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 and solar irradiation maps. They indicate 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.