Two-step artificial neural network to estimate the solar radiation at Java Island

Adi Kurniawan, Eiji Shintaku


The availability of information about solar radiation characteristics, particularly solar radiation predictions, is important for efficiently designing solar energy systems. Solar radiation information is not available in Indonesia because official measurements have not been conducted by the Indonesian Meteorological, Climatology, and Geophysical Agency (BMKG). In this study, a new two-step artificial neural network (ANN) is proposed to estimate both the daily average and hourly solar radiation at Java Island, Indonesia. The input parameters for the daily average solar radiation estimation are the location and time required, along with five selected monthly meteorological parameters that BMKG predicts for the subsequent month. The selected meteorological parameters are temperatures, relative humidity, and precipitation. The estimated daily average solar radiation is then used as the input parameter of the hourly solar radiation estimation along with the local time and location. The ANN training was conducted using two years of data, 2018 and 2019, from Surabaya and Jakarta, while the validation was performed in the same cities for January through July 2020. The accuracy of the proposed method is comparable to previous studies with an average R2 of 98.70% for the daily average solar radiation estimate and 97.44% for the hourly solar radiation estimate.


daily average solar radiation; energy forecasting; global solar radiation; hourly solar radiation; multilayer feed-forward;

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).