Komi Apélété AMOU^1,, Tchamye Tcha-Esso BOROZE¹, Sanoussi OURO-DJOBO¹, Koffi SAGNA¹, Yaovi Ouézou AZOUMA¹, Magolmèèna BANNA¹, Kossi NAPO¹

This paper presents a new neural network approach for the generation of synthetic monthly radiation data for nine localities in Togo. The neural model employed is the well-known Multi-Layer Perceptron (MLP) paradigm, in feedback architecture, using a record of historical values for the supervised network training. The method is based on the MLP ability to extract, from a sufficiently general training set, the existing relationships between variables whose interdependence is unknown a priori. Simulation results are compared to the measured values for the three towns where solar irradiation is measured in Togo. The results show that the generated values are of the real values. The method has been developed using data values from Lomé, Atakpamé and Mango, and is generalized to generate data of any location for the establishment of solar maps. Indeed, the proposed methodology is of general applicability to the estimation of highly complex temporal series.

estimation, neural model multi, Layer Perceptron (MLP), solar radiation maps