Modeling of Daily Solar Energy on a Horizontal Surface for Five Main Sites in Malaysia

This paper presents models for global and diffuse solar energy on a horizontal surface for main five sites in Malaysia. The global solar energy is modeled using linear, nonlinear, fuzzy logic, and artificial neural network (ANN) models, while the diffuse solar energy is modeled using linear, nonlinear, and ANN models. Three statistical values are used to evaluate the developed solar energy models, namely, the mean absolute percentage error, MAPE; root mean square error, RMSE; and mean bias error, MBE. The results showed that the ANN models are superior compared with the other models in which the MAPE in calculating the global solar energy in Malaysia by the ANN model is 5.38%, while the MAPE for the linear, nonlinear, and fuzzy logic models are 8.13%, 6.93%, and 6.71%, respectively. The results for the diffuse solar energy showed that the MAPE of the ANN model is 1.53%, while the MAPE of the linear and nonlinear models are 4.35% and 3.74%, respectively. The accurate ANN models can therefore be used to predict solar energy in Malaysia and nearby regions.

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