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• W2908151887 endingPage "148" @default.
• W2908151887 startingPage "148" @default.
• W2908151887 abstract "More accurate data of hourly Global Horizontal Irradiance (GHI) are required in the field of solar energy in areas with limited ground measurements. The aim of the research was to obtain more precise and accurate hourly GHI by using new input from Satellite-Derived Datasets (SDDs) with new input combinations of clear sky (Cs) and top-of-atmosphere (TOA) irradiance on the horizontal surface and with observed climate variables, namely Sunshine Duration (SD), Air Temperature (AT), Relative Humidity (RH) and Wind Speed (WS). The variables were placed in ten different sets as models in an artificial neural network with the Levenberg–Marquardt training algorithm to obtain results from training, validation and test data. It was applied at two station types in northeast Iraq. The test data results with observed input variables (correlation coefficient (r) = 0.755, Root Mean Square Error (RMSE) = 33.7% and bias = 0.3%) are improved with new input combinations for all variables (r = 0.983, RMSE = 9.5% and bias = 0.0%) at four automatic stations. Similarly, they improved at five tower stations with no recorded SD (from: r = 0.601, RMSE = 41% and bias = 0.7% to: r = 0.976, RMSE = 11.2% and bias = 0.0%). The estimation of hourly GHI is slightly enhanced by using the new inputs." @default.
• W2908151887 created "2019-01-11" @default.
• W2908151887 creator A5011014550 @default.
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• W2908151887 creator A5018226678 @default.
• W2908151887 creator A5063148857 @default.
• W2908151887 date "2019-01-02" @default.
• W2908151887 modified "2023-09-26" @default.
• W2908151887 title "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks" @default.
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• W2908151887 doi "https://doi.org/10.3390/en12010148" @default.
• W2908151887 hasPublicationYear "2019" @default.
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