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
  • [31] Assessment and Mapping of Solar Energy Potential Using Artificial Neural Network and GIS Technology in the Southern Part of India
    Anwar, Khalid
    Deshmukh, Sandip
    INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2018, 8 (02): : 974 - 985
  • [32] An Artificial Neural Network Approach for Underwater Warp Prediction
    Halder, Kalyan Kumar
    Tahtali, Murat
    Anavatti, Sreenatha G.
    ARTIFICIAL INTELLIGENCE: METHODS AND APPLICATIONS, 2014, 8445 : 384 - 394
  • [33] Mapping of the Solar Irradiance in the UAE Using Advanced Artificial Neural Network Ensemble
    Alobaidi, Mohammad H.
    Marpu, Prashanth R.
    Ouarda, Taha B. M. J.
    Ghedira, Hosni
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2014, 7 (08) : 3668 - 3680
  • [34] Application of Artificial Neural Network for Prediction of Liquid Viscosity
    Moghadassi, Abdolreza
    Parvizian, Fahime
    Hosseini, Sayed
    INDIAN CHEMICAL ENGINEER, 2010, 52 (01) : 37 - 48
  • [35] Prediction of air pollutants by using an artificial neural network
    Sohn, SH
    Oh, SC
    Yeo, YK
    KOREAN JOURNAL OF CHEMICAL ENGINEERING, 1999, 16 (03) : 382 - 387
  • [36] Application of Artificial Neural Network in the Prediction of Output in Oilfield
    Zhu, Changjun
    Zhao, Xiujuan
    FIRST IITA INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2009, : 155 - 158
  • [37] Prediction of air pollutants by using an artificial neural network
    Sang Hyun Sohn
    Sea Cheon Oh
    Yeong-Koo Yeo
    Korean Journal of Chemical Engineering, 1999, 16 : 382 - 387
  • [38] A comparison study based on artificial neural network for assessing PV/T solar energy production
    Yousif, Jabar H.
    Kazem, Hussein A.
    Alattar, Nebras N.
    Elhassan, Imadeldin I.
    CASE STUDIES IN THERMAL ENGINEERING, 2019, 13
  • [39] Neural Network Modelling for Prediction of Zeta Potential
    Marsalek, Roman
    Kotyrba, Martin
    Volna, Eva
    Jarusek, Robert
    MATHEMATICS, 2021, 9 (23)
  • [40] PREDICTION OF ROCKBURST BY ARTIFICIAL NEURAL NETWORK
    ChenHaijun LiNenghui NieDexin Shang Yuequan Department of Geotechnical Engineering Nanjing Hydraulic Research Institute Nanjing China Department of Geotechnical Engineering Tongji University Shanghai China Institute of Engineering Geology Chengdu University of Technology Chengdu China Institute of Disaster Prevention Zhejian University Hangzhou China
    岩石力学与工程学报, 2003, (05) : 762 - 768
12345