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
  • [21] Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks
    Chiteka, K.
    Enweremadu, C. C.
    JOURNAL OF CLEANER PRODUCTION, 2016, 135 : 701 - 711
  • [22] Artificial neural networks in outcome prediction
    Lundin, J
    ANNALES CHIRURGIAE ET GYNAECOLOGIAE, 1998, 87 (02) : 128 - 130
  • [23] PREDICTION OF HOURLY SOLAR RADIATION USING AN ARTIFICIAL NEURAL NETWORK
    Solmaz, Ozgur
    Ozgoren, Muammer
    MENDEL 2011 - 17TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING, 2011, : 218 - 225
  • [24] Predicting solar energy generation through artificial neural networks using weather forecasts for microgrid control
    Rodriguez, Fermin
    Fleetwood, Alice
    Galarza, Ainhoa
    Fontan, Luis
    RENEWABLE ENERGY, 2018, 126 : 855 - 864
  • [25] Solar Cycle Signal in Climate and Artificial Neural Networks Forecasting
    Tzanis, Chris G.
    Benetatos, Charilaos
    Philippopoulos, Kostas
    REMOTE SENSING, 2022, 14 (03)
  • [26] Modeling of Solar Energy Potential in Libya using an Artificial Neural Network Model
    Kutucu, Hakan
    Almryad, Ayad
    PROCEEDINGS OF THE 2016 IEEE FIRST INTERNATIONAL CONFERENCE ON DATA STREAM MINING & PROCESSING (DSMP), 2016, : 356 - 359
  • [27] Mapping of solar energy potential in Fiji using an artificial neural network approach
    Oyewola, Olanrewaju M.
    Ismail, Olawale S.
    Olasinde, Malik O.
    Ajide, Olusegun O.
    HELIYON, 2022, 8 (07)
  • [28] Solar Energy Potential Forecasting and Optimization Using Artificial Neural Network: South Africa Case Study
    Leholo, Sempe
    Owolawi, Pius
    Akindeji, Kayode
    PROCEEDINGS 2019 AMITY INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AICAI), 2019, : 533 - 536
  • [29] PREDICTION OF POTENTIAL HIT SONG AND MUSICAL GENRE USING ARTIFICIAL NEURAL NETWORKS
    Monterola, Christopher
    Abundo, Cheryl
    Tugaff, Jeric
    Venturina, Lorcel Ericka
    INTERNATIONAL JOURNAL OF MODERN PHYSICS C, 2009, 20 (11): : 1697 - 1718
  • [30] Artificial Neural Networks in Cancer Recurrence Prediction
    Ritthipravat, Panrasee
    2009 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND TECHNOLOGY, VOL II, PROCEEDINGS, 2009, : 103 - 107