Estimation of solar radiation components incident on Helwan site using neural networks

Field testing carried out for solar energy applications is costly, time consuming and depends heavily on prevailing weather conditions. Adequate security and weather protection must be provided at the test site. Measurements may also suffer from delays that can be caused by system failures and bad weather. To overcome these problems the need for accurate model becomes evermore important. To achieve such prediction task, an artificial neural network, ANN, model is regarded as a cost-effective technique superior to traditional statistical methods. In this paper, Levenberg optimization function is adopted to predict insolation data in different spectral bands for Helwan (Egypt) monitoring station. The predicted values were then compared with the actual values and presented in terms of usual statistics. The results hint that, the ANN model predicted infrared, ultraviolet, and global insolation with a good accuracy of approximately 95%, 93% and 96%, respectively. In addition, ANN model was tested to predict the same components for Aswan over an 11 month period. The predicted values of the ANN model compared to the actual values for Aswan produced an accuracy of 95%, 91% and 92%, respectively. Data for Aswan were not included as a part of ANN training set. Hence, these results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates.