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Prediction of Solar Energy Potential with Artificial Neural Networks

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Environmentally-Benign Energy Solutions

Part of the book series: Green Energy and Technology ((GREEN))

Abstract

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.

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Author information

Authors and Affiliations

  1. Department of Marine Engineering, Dokuz Eylul University, Izmir, Turkey

    Burak Goksu, Murat Bayraktar & Murat Pamik

  2. Department of Marine Engineering, Bulent Ecevit University, Zonguldak, Turkey

    Burak Goksu & Murat Bayraktar

Authors
  1. Burak Goksu
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  2. Murat Bayraktar
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  3. Murat Pamik
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Corresponding author

Correspondence to Murat Bayraktar .

Editor information

Editors and Affiliations

  1. Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, ON, Canada

    Ibrahim Dincer

  2. Dokuz Eylul University, Buca, Izmir, Turkey

    Can Ozgur Colpan

  3. Faculty of Engineering, Department of Mechanical Engineering, Dokuz Eylul University, Buca, Izmir, Turkey

    Mehmet Akif Ezan

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Cite this paper

Goksu, B., Bayraktar, M., Pamik, M. (2020). Prediction of Solar Energy Potential with Artificial Neural Networks. In: Dincer, I., Colpan, C., Ezan, M. (eds) Environmentally-Benign Energy Solutions. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-20637-6_13

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  • DOI: https://doi.org/10.1007/978-3-030-20637-6_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20636-9

  • Online ISBN: 978-3-030-20637-6

  • eBook Packages: EnergyEnergy (R0)

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