Artificial Neural Network Prediction to Identify Solar Energy Potential In Eastern Indonesia

被引：0

作者：

Aryani, Dharma ^{[1
]}

Pranoto, Sarwo ^{[2
]}

Fajar ^{[3
]}

Intang, A. Nur ^{[1
]}

Rhamadhan, Firza Zulmi ^{[4
]}

机构：

[1] State Polytech Ujung Pandang, Dept Elect Engn, Makassar, Indonesia

[2] Yogyakarta State Univ, Dept Elect Engn Educ, Yogyakarta, Indonesia

[3] State Polytech Ujung Pandang, Dept Chem Engn, Makassar, Indonesia

[4] PT Pembangkit Jawa Bali, Surabaya, Indonesia

来源：

2023 IEEE 3RD INTERNATIONAL CONFERENCE IN POWER ENGINEERING APPLICATIONS, ICPEA | 2023年

关键词：

artificial neural network; prediction; solar energy; ELECTRICITY; RADIATION;

D O I：

10.1109/ICPEA56918.2023.10093184

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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 clima- tological 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.

引用

页码：252 / 256

页数：5