Dharma Aryani, Sarwo Pranoto, Fajar Fajar, A. Nur Intang, Firza Zulmi Rhamadhan
The geographic location of Indonesia which climates almost entirely tropical provides exclusive potential for solar energy all through the year. This paper performs identification and prediction of solar irradiance in Eastern area of Indonesia. Modeling and estimation approach is carried out by using Artificial Neural Network (ANN) algorithm. Datasets for training and testing are highly correlated parameters from NASA climatological database for 20 years of historical data. The results of training and testing procedures in ANN show high accuracy of solar modelling and prediction. The study produces spatial mapping of solar irradiance intensity for the monthly average solar irradiance of 174 districts in Eastern Indonesia region. © 2023 IEEE.
State Polytechnic of Ujung Pandang, Department of Electrical Engineering, Makassar, Indonesia; Yogyakarta State University, Department of Electrical Engineering Education, Yogyakarta, Indonesia; State Polytechnic of Ujung Pandang, Department of Chemical Engineering, Makassar, Indonesia; Pt Pembangkit Jawa Bali, Surabaya, Indonesia
