Performance Ranking Of Artificial Neural Network Learning Algorithlvis In Solar Radiatio!\ Forecast

ABSTRACT

Artificial Neural Networks (ANNs) area prormsing alternative to conventional tools in

modeling and prediction of complex and non-linear parameters. However, the selection of

appropriate network parameters for optimum performance pose application challenges In this

study, the modeling and predictive performances of six backpropagation learning algorithms:

Levenberg-Marquardt (LM), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP),

Fletcher-Powell Conjugate Gradient (CGF), Variable Learning Rate Backpropagation (GDX)

and Bayesian Reglarization (BR) in solar radiation forecast were investigated.

Multilayer perceptron (MPL) neural network with five, ten and one nc.uronfs) in the input,

hidden and output layers, respectively was designed with MATLAB® neural network toolkit

and trained with the six learning algorithms using the daily global solar radiation data of

Ibadan (Lat. 7.40 N; Long. 3.90 E; Alt. 227.2m), Nigeria. The network performance was

ranked based on the number of iterations required for convergence, and coefficient of

correlation (r-value), mean square error (MSE) and mean absolute percentage error (MAPE)

between the actual and predicted values of the training and testing datasets. Results showed

that the LM and BR learning algorithms are the two best algorithms to be considered for use

in modeling and forecasting of solar radiation data.

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