Prediction of Solar Energy Potential with Artificial Neural Networks

被引:1
|
作者
Goksu, Burak [1 ,2 ]
Bayraktar, Murat [1 ,2 ]
Pamik, Murat [1 ]
机构
[1] Dokuz Eylul Univ, Dept Marine Engn, Izmir, Turkey
[2] Bulent Ecevit Univ, Dept Marine Engn, Zonguldak, Turkey
来源
ENVIRONMENTALLY-BENIGN ENERGY SOLUTIONS | 2020年
关键词
Neural networks; Emissions; Energy saving; Solar energy; RADIATION; TEMPERATURE; HUMIDITY;
D O I
10.1007/978-3-030-20637-6_13
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The energy requirements have been met from fossil fuels since the early 1800s. Considering the environmental awareness and limited fossil resources, using renewable energy resources are compulsory to meet the increasing energy demand. Solar and wind energy, biofuels, and natural gas are leading ones. Solar energy is an effective and clean energy source compared in terms of sustainability, reliability, and economy. In the maritime sector, eco-friendly and sustainable qualities are sought in all of the efforts to reduce costs. Therefore, in many maritime fields, solar energy is used as an alternative energy source. The purpose of this study is achieving maximum efficiency from solar panels by using optimization technique. The energy estimation was performed by artificial neural networks method on solar panels based on weather changes in Izmir Gulf. The results are compared with the "Renewable Energy General Administration" data of Turkey. As a result, the obtained data will be informative to the researcher who will study solar energy's maritime applications. Besides, this study will be a possible source to make comparisons with similar solar energy studies.
引用
收藏
页码:247 / 258
页数:12
相关论文
共 50 条
  • [1] Use of Artificial Neural Networks for Prediction of Solar Energy Potential in Southern States of India
    Anwar, Khalid
    Deshmukh, Sandip
    PROCEEDINGS OF 2018 2ND INTERNATIONAL CONFERENCE ON GREEN ENERGY AND APPLICATIONS (ICGEA), 2018, : 63 - 68
  • [2] Solar Energy Prediction for Malaysia Using Artificial Neural Networks
    Khatib, Tamer
    Mohamed, Azah
    Sopian, K.
    Mahmoud, M.
    INTERNATIONAL JOURNAL OF PHOTOENERGY, 2012, 2012
  • [3] Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data
    Manuel Barrera, Jose
    Reina, Alejandro
    Mate, Alejandro
    Carlos Trujillo, Juan
    SUSTAINABILITY, 2020, 12 (17)
  • [4] Artificial Neural Network Prediction to Identify Solar Energy Potential In Eastern Indonesia
    Aryani, Dharma
    Pranoto, Sarwo
    Fajar
    Intang, A. Nur
    Rhamadhan, Firza Zulmi
    2023 IEEE 3RD INTERNATIONAL CONFERENCE IN POWER ENGINEERING APPLICATIONS, ICPEA, 2023, : 252 - 256
  • [5] 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
  • [6] Solar Irradiance Fluctuation Prediction Methodology Using Artificial Neural Networks
    Kamadinata, Jane Oktavia
    Ken, Tan Lit
    Suwa, Tohru
    JOURNAL OF SOLAR ENERGY ENGINEERING-TRANSACTIONS OF THE ASME, 2020, 142 (03):
  • [7] Prediction of Solar Radiation Using Artificial Neural Networks
    Faceira, Joao
    Afonso, Paulo
    Salgado, Paulo
    CONTROLO'2014 - PROCEEDINGS OF THE 11TH PORTUGUESE CONFERENCE ON AUTOMATIC CONTROL, 2015, 321 : 397 - 406
  • [8] A Novel Approach for Solar Radiation Prediction Using Artificial Neural Networks
    Khatib, T.
    ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, 2015, 37 (22) : 2429 - 2436
  • [9] Prediction of Hourly Solar Radiation in Six Provinces in Turkey by Artificial Neural Networks
    Solmaz, Ozgur
    Ozgoren, Muammer
    JOURNAL OF ENERGY ENGINEERING-ASCE, 2012, 138 (04): : 194 - 204
  • [10] Evolutionary artificial neural networks for accurate solar radiation prediction
    Guijo-Rubio, D.
    Duran-Rosal, A. M.
    Gutierrez, P. A.
    Gomez-Orellana, A. M.
    Casanova-Mateo, C.
    Sanz-Justo, J.
    Salcedo-Sanz, S.
    Hervas-Martinez, C.
    ENERGY, 2020, 210 (210)