Solar radiation modelling using ANNs for different climates in China

Abstract: Artificial neural networks (ANNs) were used to develop prediction models for daily global solar radiation using measured sunshine duration for 40 cities covering nine major thermal climatic zones and sub-zones in China. Coefficients of determination (R 2) for all the 40 cities and nine climatic zones/sub-zones are 0.82 or higher, indicating reasonably strong correlation between daily solar radiation and the corresponding sunshine hours. Mean bias error (MBE) varies from −3.3MJ/m2 in Ruoqiang (cold climates) to 2.19MJ/m2 in Anyang (cold climates). Root mean square error (RMSE) ranges from 1.4MJ/m2 in Altay (severe cold climates) to 4.01MJ/m2 in Ruoqiang. The three principal statistics (i.e., R 2, MBE and RMSE) of the climatic zone/sub-zone ANN models are very close to the corresponding zone/sub-zone averages of the individual city ANN models, suggesting that climatic zone ANN models could be used to estimate global solar radiation for locations within the respective zones/sub-zones where only measured sunshine duration data are available. [Copyright &y& Elsevier]