Application of NNARX in Modeling a Solar Radiation Prediction

Solar radiation mapping has used geographical and meteorological data. To obtain geographical and meteorological data, a Geographic Information System (GIS) is required. GIS is defined as an integrated geographic resource that presents data in terms of spatial information. This data is important for Neural Networks as it will be used as input parameters for the development of solar radiation prediction models. Solar radiation prediction is one way to map the sun's rays in certain places where there are insufficient resources or space to build a complete solar radiation measurement station. Since predictions about solar radiation require meteorological and geographical data, this paper will give an overview of GIS-assisted geographical and meteorological data to be used as input parameters for solar radiation mapping which will eventually be used as input for prediction models developed for the whole country of Malaysia using Neural Networks. Based on the results, the prediction model developed managed to obtain a coefficient of determination, R2 value of 0.9329.

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Application of NNARX in Modeling a Solar Radiation Prediction

Cited by 3 publications

References 23 publications

Analysis of Training Function for NNARX in Solar Radiation Prediction Modeling

Analysis of Training Function for NNARX in Solar Radiation Prediction Modeling

Analysis on Parameter Effect for Solar Radiation Prediction Modeling using NNARX

Gis-Aided Geographical and Meteorological Data Overview of Solar Radiation Mapping for Malaysia – An Explanatory Study Based on Solar Radiation Prediction Modeling Using Neural Network Approach

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