Predicting Global Solar Radiation Using an Artificial Neural Network Single‐Parameter Model

Karoro Angela,Mubiru James,Ssenyonga Taddeo

doi:10.1155/2011/751908

Abstract

We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19°N, a longitude of 32.34°E, and an altitude of 1200 m above sea level. The five‐year data was split into two parts in 2003–2006 and 2007‐2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed‐forward back‐propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m2 and a root mean square error of 0.521 MJ/m2. The single‐parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours).

Highlights

Solar energy is energy from the Sun and is a vital resource to plant and human life on the Earth’s surface
The artificial neural networks (ANNs) model developed to date to estimate solar radiation in Uganda is based on several input parameters
The results showed that the ANN model is capable of generating global solar radiation values at places where monitoring stations were not established

Summary

Introduction

Solar energy is energy from the Sun and is a vital resource to plant and human life on the Earth’s surface. Solar radiation is a critical input parameter in these analyses [1] All this requires knowledge of distribution of solar energy; there is a need for availability of solar radiation data. This data can be measured or estimated from appropriate models. In developing countries such as Uganda, solar radiation data is scarce due to the high costs involved in buying and maintaining solar measuring equipment. The ANN model developed to date to estimate solar radiation in Uganda is based on several input parameters. These parameters are not readily available at a number of stations. We used data covering a period of five years; the several-parameter model used three years’ data

Review on ANN Solar Radiation Models

Processing the Artificial Neural Network

Error Analysis

Results and Discussions

Conclusions