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Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models

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  • Jiang, Yingni

Abstract

An artificial neural network (ANN) model for estimating monthly mean daily diffuse solar radiation is presented in this paper. Solar radiation data from 9 stations having different climatic conditions all over China during 1995-2004 are used for training and testing the ANN. Solar radiation data from eight typical cities are used for training the neural networks and data from the remaining one location are used for testing the estimated values. Estimated values are compared with measured values in terms of mean percentage error (MPE), mean bias error (MBE) and root mean square error (RMSE). The results of the ANN model have been compared with other empirical regression models. The solar radiation estimations by ANN are in good agreement with the actual values and are superior to those of other available models. In addition, ANN model is tested to predict the same components for Zhengzhou station over the same period. Results indicate that ANN model predicts the actual values for Zhengzhou with a good accuracy of 94.81%. Data for Zhengzhou are not included as a part of ANN training set. Hence, these results demonstrate the generalization capability of this approach and its ability to produce accurate estimates in China.

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  • Jiang, Yingni, 2008. "Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models," Energy Policy, Elsevier, vol. 36(10), pages 3833-3837, October.
    • Handle: RePEc:eee:enepol:v:36:y:2008:i:10:p:3833-3837
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