Advertisement

Prediction of Solar Energy Potential with Artificial Neural Networks

  • Burak Goksu
  • Murat BayraktarEmail author
  • Murat Pamik
Conference paper
Part of the Green Energy and Technology book series (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.

Keywords

Neural networks Emissions Energy saving Solar energy 

References

  1. 1.
    Şenkal O, Kuleli T (2009) Estimation of solar radiation over Turkey using artificial neural network and satellite data. Appl Energy 86(7–8):1222–1228CrossRefGoogle Scholar
  2. 2.
    Karademir A (2015) Transformatör T-bağlantı yapısının çekirdek kayıplarına etkisiGoogle Scholar
  3. 3.
    Timmons D, Harris JM, Roach B (2014) The economics of renewable energy. Global Development and Environment Institute, Tufts University, 52Google Scholar
  4. 4.
    Imteaz MA, Ahsan A (2018) Solar panels: real efficiencies, potential productions and payback periods for major Australian cities. Sustain Energy Technol Assess 25:119–125Google Scholar
  5. 5.
    Roos CJ (2009) Solar electric system design, operation and installation: an overview for builders in the US Pacific NorthwestGoogle Scholar
  6. 6.
    Report on Solar Energy Storage Methods and Life Cycle Assessment. http://www.energy.ca.gov/2013publications/CEC-500-2013-073/CEC-500-2013-073.pdf. Last accessed 2018/02/02
  7. 7.
    Rahman MM, Islam AS, Salehin S, Al-Matin MA (2016) Development of a model for techno-economic assessment of a stand-alone off-grid solar photovoltaic system in Bangladesh. Int J Renew Energy Res (IJRER) 6(1):140–149Google Scholar
  8. 8.
    Renewable Energy Prospects for the European Union. http://www.irena.org/publications/2018/Feb/Renewable-energy-prospects-for-the-EU. Last accessed 2018/02/02
  9. 9.
    Xu W, Mu C, Tang L (2016) Advanced control techniques for PV maximum power point tracking. In: Advances in solar photovoltaic power plants. Springer, Berlin, Heidelberg, pp 43–78Google Scholar
  10. 10.
    Kabir E, Kumar P, Kumar S, Adelodun AA, Kim KH (2018) Solar energy: potential and future prospects. Renew Sustain Energy Rev 82:894–900CrossRefGoogle Scholar
  11. 11.
    Bouzgou H, Gueymard CA (2019) Fast short-term global solar irradiance forecasting with wrapper mutual information. Renew Energy 133:1055–1065CrossRefGoogle Scholar
  12. 12.
    Notton G, Voyant C, Fouilloy A, Duchaud JL, Nivet ML (2019) Some applications of ANN to solar radiation estimation and forecasting for energy applications. Appl Sci 9(1):209CrossRefGoogle Scholar
  13. 13.
    Feng J, Wang W, Li J (2018) An LM-BP neural network approach to estimate monthly-mean daily global solar radiation using modis atmospheric products. Energies 11(12):3510CrossRefGoogle Scholar
  14. 14.
    Benali L, Notton G, Fouilloy A, Voyant C, Dizene R (2019) Solar radiation forecasting using artificial neural network and random forest methods: application to normal beam, horizontal diffuse and global components. Renew Energy 132:871–884CrossRefGoogle Scholar
  15. 15.
    Doorga JR, Rughooputh SD, Boojhawon R (2019) Modelling the global solar radiation climate of Mauritius using regression techniques. Renew Energy 131:861–878CrossRefGoogle Scholar
  16. 16.
    Thornton PE, Running SW (1999) An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agric For Meteorol 93(4):211–228CrossRefGoogle Scholar
  17. 17.
    Mellit A, Pavan AM (2010) A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy 84(5):807–821CrossRefGoogle Scholar
  18. 18.
    Thornton PE, Hasenauer H, White MA (2000) Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria. Agric For Meteorol 104(4):255–271CrossRefGoogle Scholar
  19. 19.
    Çelik Ö, Teke A, Yıldırım HB (2016) The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey. J Clean Prod 116:1–12CrossRefGoogle Scholar
  20. 20.
    Olatomiwa L, Mekhilef S, Shamshirband S, Petković D (2015) Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria. Renew Sustain Energy Rev 51:1784–1791CrossRefGoogle Scholar
  21. 21.
    Trapero JR, Kourentzes N, Martin A (2015) Short-term solar irradiation forecasting based on dynamic harmonic regression. Energy 84:289–295CrossRefGoogle Scholar
  22. 22.
    Öztemel E (2012) Yapay sinir ağlari. PapatyaYayincilik, IstanbulGoogle Scholar
  23. 23.
    Azar AT, Vaidyanathan S (2015) Computational intelligence applications in modeling and control. Springer International PublishingGoogle Scholar
  24. 24.
    Yegnanarayana B (2009) Artificial neural networks. PHI Learning Pvt. Ltd.Google Scholar
  25. 25.
    Yüksek AG (2007) Hava kirliliği tahmininde çoklu regresyon analizi ve yapay sinir ağları yönteminin karşılaştırılması. Doctoral dissertation, Cumhuriyet Üniversitesi, SivasGoogle Scholar
  26. 26.
    Demuth HB, Beale MH, De Jess O, Hagan MT (2014) Neural network design. Martin HaganGoogle Scholar
  27. 27.
    Samarasinghe S (2016) Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Auerbach PublicationsGoogle Scholar
  28. 28.
    Türkiye Global Güneş Radyasyonu uzun yıllar ortalaması (2004–2016) Heliosat Model Ürünleri, https://www.mgm.gov.tr/kurumici/radyasyon_iller.aspx. Last accessed 2018/02/02
  29. 29.
    İzmir Aylık Hava Durumu. https://weather.com/tr. Last accessed 2018/02/01
  30. 30.
    Lourakis MI (2005) A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Found Res Technol 4(1):1–6Google Scholar
  31. 31.
    Rafiq MY, Bugmann G, Easterbrook DJ (2001) Neural network design for engineering applications. Comput Struct 79(17):1541–1552CrossRefGoogle Scholar
  32. 32.
    Wilamowski BM, Yu H (2010) Improved computation for Levenberg–Marquardt training. IEEE Trans Neural Netw 21(6):930–937CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Marine EngineeringDokuz Eylul UniversityIzmirTurkey
  2. 2.Department of Marine EngineeringBulent Ecevit UniversityZonguldakTurkey

Personalised recommendations