Solar Radiation Estimation with Neural Network Approach Using Meteorological Data in Indonesia

The objective of this study is to determine the solar energy potential in Indonesia using artificial neural networks (ANNs) approach. In this study, the meteorological data during 2005 to 2009 from 3 cities (Jakarta, Manado, Bengkulu) are used for training the neural networks and the data from 1 city (Makasar) is used for testing the estimated values. The testing data are not used in the training of the network in order to give an indication of the performance of the system at unknown locations. Fifteen combinations of ANN models were developed and evaluated. The multi layer perceptron ANNs model, with 7 inputs variables (average temperature, average relative humidity, average sunshine duration, longitude, latitude, latitude, month of the year) are proposed to estimate the global solar irradiation as output. To evaluate the performance of ANN models, statistical error analyses in terms of mean absolute percentage error (MAPE) are conducted for testing data. The best result of MAPE are found to be 7.4% when 7 neurons were set up in the hidden layer. The result demonstrates the capability of ANN approach to generate the solar radiation estimation in Indonesia using meteorological data.