An Estimation of hourly average solar radiation using artificial neural network in the city of Surabaya

Abstract

To maximize the effectiveness of solar energy systems installed on the ship, the panels need to be positioned on their optimal inclination angle. To determine this angle, information on hourly average solar radiation is required in addition to position and direction data. However, the amount of hourly solar radiation is not continuously measured in Indonesia. This study proposed a method to estimate the hourly solar radiation for any month. This is based on Artificial Neural Network (ANN) with the month and the hour required for the estimate and the previous daily average solar radiation as the input. The proposed ANN method was validated by comparing the estimation results with the measured hourly average solar radiation in Surabaya from May to July 2020. Its effectiveness was affirmed with the coefficient of determination (R2) of 0.983 or higher for each of the three observed months.