A Method to Estimation of Global Solar Radiation with Meteorological Parameters under Cloudless Sky Condition using Artificial Neural Network - Choosri, Pranomkorn; Foobunma, Kanmanee; Kongsomlit, Arisara (Naresuan University Journal: Science and Technology (NUJST); Vol 29 No 4 (2021): October-December 2021, 2021)
In this work, variation of global radiation hourly basis and model developed to estimate the radiation under cloudless sky condition were purposed at Lopburi province (14.83°N, 100.62°E). Data of global radiation and meteorological parameters from 2012 to 2017 were investigated. For the variation of global radiation, the maximum and minimum of radiation are 3.46 MJ/m 2 in April and 2.40 MJ/m 2 in December, and there are some meteorological parameters influencing on the radiation. Therefore, a model for estimation of hourly global solar radiation under cloudless sky condition at this site was proposed based on the artificial neural network (ANN). This ANN has one input layer, two hidden layers and one output layer. The input layer consists of some meteorological parameters that are solar zenith angle, visibility, air temperature, relative humidity, wind speed and air pressure, and the output layer is global solar radiation under clear sky condition. The ANN was trained using the input and output data collected at Lopburi meteorological station during the year: 2012-2014. It was then validated against an independent data at the site for the period of three years (2015-2017). The validation result indicates that the estimated solar radiation under clear sky condition obtained from ANN are in good agreement with that from the measurement, with root mean square difference (RMSD) of 8.52% and mean bias difference (MBD) of 1.22%. Therefore, the model can be applied for estimation of global radiation under cloudless sky condition at other meteorological stations with similar climate. The estimated solar radiation data are useful for management in solar power plant, solar thermal energy system and also for the studies in the atmospheric field.  Keywords: solar radiation, meteorological parameter, artificial neural network