An application of the Multilayer Perceptron: Estimation of Global Solar Radiation and the Establishment of Solar Radiation Maps of Togo

Komi Apélété Amou,Kossi Napo,Sanoussi Ouro-Djobo,Koffi Sagna,Tchamye Tcha-Esso Boroze,Yaovi Ouézou Azouma,Magolmèèna Banna

doi:10.12691/rse-5-1-2

Abstract

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.

Highlights

Due to uncertainty of availability of fossil fuels, increasing environmental pollution and general awareness amongst the common people, the green sources of energy are being encouraged
Artificial Neural Network (ANN) methods have been employed for the prediction of global solar radiation both in time and special domains
Multi-Layer Perceptron (MLP) is used for the estimation of global solar radiation (GSR) based on measured temperature, relative humidity and latitude data

Summary

Introduction

Due to uncertainty of availability of fossil fuels, increasing environmental pollution and general awareness amongst the common people, the green sources of energy are being encouraged. The most common is in tabular form with a lot of very useful information, usually large solar radiation sequences, but extremely difficult to handle Another method can be solar radiation maps of the zone where the installation is going to be made. To obtain a solar radiation map it is necessary to know the solar radiation of many points spread wide across the real zone of the map where it is going to be drawn These solar data can be available in several time scales. The present paper utilizes the air temperature, relative humidity and the latitude of Lomé, Atakpamé and Mango data as input in neural networks for the prediction of global solar irradiation (GSR) on horizontal surfaces in seven other cities in Togo, and to establish its solar maps

Methods

Results

Conclusion