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
收藏
页码：252 / 256
页数：5
相关论文
共 50 条
  • [41] Prediction of rockburst by artificial neural network
    Chen, H., 1600, Academia Sinica (22):
  • [42] A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources
    Ferrero Bermejo, Jesus
    Gomez Fernandez, Juan F.
    Olivencia Polo, Fernando
    Crespo Marquez, Adolfo
    APPLIED SCIENCES-BASEL, 2019, 9 (09):
  • [43] Indonesia's Vast Solar Energy Potential
    Silalahi, David Firnando
    Blakers, Andrew
    Stocks, Matthew
    Lu, Bin
    Cheng, Cheng
    Hayes, Liam
    ENERGIES, 2021, 14 (17)
  • [44] Artificial Neural Network based Controller for Energy Management in a Solar Home in Algeria
    Chekired, Fathia
    Berkane, Smain
    Meflah, Aissa
    Mahrane, Achour
    PROCEEDINGS OF 2019 7TH INTERNATIONAL RENEWABLE AND SUSTAINABLE ENERGY CONFERENCE (IRSEC), 2019, : 135 - 139
  • [45] Design of Solar Energy Based System Using Artificial Neural Network Controller
    Vinita
    Nain, Abhimanyu
    Saroha, Sumit
    JOURNAL OF ELECTRICAL SYSTEMS, 2024, 20 (05) : 282 - 289
  • [46] Modeling of solar energy systems using artificial neural network: A comprehensive review
    Elsheikh, Ammar H.
    Sharshir, Swellam W.
    Abd Elaziz, Mohamed
    Kabeel, A. E.
    Wang Guilan
    Zhang Haiou
    SOLAR ENERGY, 2019, 180 : 622 - 639
  • [47] Modeling Solar Energy Potential in a Tehran Province Using Artificial Neural Networks
    Ramedani, Zeynab
    Omid, Mahmoud
    Keyhani, Alireza
    INTERNATIONAL JOURNAL OF GREEN ENERGY, 2013, 10 (04) : 427 - 441
  • [48] ARTIFICIAL NEURAL NETWORK TRAINED TO IDENTIFY MOSQUITOS IN FLIGHT
    MOORE, A
    JOURNAL OF INSECT BEHAVIOR, 1991, 4 (03) : 391 - 396
  • [49] Harvesting Solar Energy: Prediction of Daily Global Horizontal Irradiance Using Artificial Neural Networks and Assessment of Electrical Energy of Photovoltaic at North Eastern Ethiopia
    Woldegiyorgis, Tegenu A.
    Assamnew, Abera D.
    Desalegn, Gezahegn A.
    Mossie, Sentayehu Y.
    ENERGY SCIENCE & ENGINEERING, 2025, 13 (01) : 255 - 267
  • [50] Performance Prediction Model of Piezoelectric Energy Harvester Based on Artificial Neural Network
    Zhang J.
    Zhang J.
    Ning Y.
    Qu D.
    Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis, 2023, 43 (01): : 172 - 178
12345