Global and Diffuse Solar Radiation Characteristics of Bangkok and its Forecast Using Artificial Neural Network

Natthanan Chawphongphang, Pipat Chaiwiwatworakul, Surapong Chirarattananon


This paper investigated the artificial neural network (ANN) modeling to forecast the one-hour ahead global and diffuse solar radiation using the observed data during 2019-2021 at a meteorological station in Bangkok, Thailand. Results from the statistical analysis portrayed the abundance of the tropical solar radiation and its large variation all year round. As a step to move advance the renewable energy use of the solar radiation at the location, the ANN forecasting models were proposed for all climatic conditions. The model input were the ambient temperature, solar elevation, and an insolation parameter namely normalized global solar radiation. The model performance was assessed by using the two statistic parameters: normalized mean bias error (nMBE), and normalized root mean square error (nRMSE). The results showed that the ANN models could forecast the global radiation with the nMBE of 1.79% and nRMSE of 26.20%. For the diffuse radiation forecast, the nMBE and nRMSE of the models were obtained at 7.78% and 36.21%, respectively. In order to demonstrate a benefit of the two developed ANN models, their forecasted horizontal data were employed to further predict the total solar radiation on vertical plane. Benchmarked with the smart persistence model, the developed ANN models possessed the higher forecasting accuracy for all weather conditions.


Artificial neural network; Diffuse radiation; Forecast; Global radiation; Tropical

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