Advertisement

Application of Neural Networks Solar Radiation Prediction for Hybrid Renewable Energy Systems

  • P. Chatziagorakis
  • C. Elmasides
  • G. Ch. Sirakoulis
  • I. Karafyllidis
  • I. Andreadis
  • N. Georgoulas
  • D. Giaouris
  • A. I. Papadopoulos
  • C. Ziogou
  • D. Ipsakis
  • S. Papadopoulou
  • P. Seferlis
  • F. Stergiopoulos
  • S. Voutetakis
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)

Abstract

In this paper a Recurrent Neural Network (RNN) for solar radiation prediction is proposed for the enhancement of the Power Management Strategies (PMSs) of Hybrid Renewable Energy Systems (HYRES). The presented RNN can offer both daily and hourly prediction concerning solar irradiation forecasting. As a result, the proposed model can be used to predict the Photovoltaic Systems output of the HYRES and provide valuable feedback for PMSs of the understudy autonomous system. To do so a flexible network based design of the HYRES is used and, moreover, applied to a specific system located on Olvio, near Xanthi, Greece, as part of SYSTEMS SUNLIGHT S.A. facilities. As a result, the RNN after training with meteorological data of the aforementioned area is applied to the specific HYRES and successfully manages to enhance and optimize its PMS based on the provided solar radiation prediction.

Keywords

Recurrent Neural Network Solar Radiation Power Management Strategy Hybrid Renewable Energy System 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Deshmukha, M.K., Deshmukh, S.S.: Modeling of hybrid renewable energy systems. Renewable and Sustainable Energy Reviews 12(1), 235–249 (2008)CrossRefGoogle Scholar
  2. 2.
    Alam, S., Kaushik, S.C., Garg, S.N.: Computation of beam solar radiation at normal incidence using artificial neural network. Renewable Energy 31(10), 1483–1491 (2006)CrossRefGoogle Scholar
  3. 3.
    Mubiru, J., Banda, E.J.K.B.: Estimation of monthly average daily global solar irradiation using artificial neural networks. Solar Energy 82(2), 181–187 (2008)CrossRefGoogle Scholar
  4. 4.
    Rehman, S., Mohandes, M.: Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy 36(2), 571–576 (2008)CrossRefGoogle Scholar
  5. 5.
    Ghanbarzadeh, A., Noghrehabadi, R., Assareh, E., Behrang, M.A.: Solar radiation forecasting using meteorological data. In: 7th IEEE International Conference on Industrial Informatics (INDIN 2009), UK, (2009)Google Scholar
  6. 6.
    Benghanem, M., Mellit, A.: Radial Basis Function Network – based prediction of global solar radiation data: Application for sizing of a stand – alone photovoltaic system at Al – Madinah, Saudi Arabia. Energy 35, 3751–3762 (2010)CrossRefGoogle Scholar
  7. 7.
    Paoli, C., Voyant, C., Muselli, M., Nivet, M.L.: Forecasting of preprocessed daily solar radiation time series using neural networks. Solar Energy 84(12), 2146–2160 (2010)CrossRefGoogle Scholar
  8. 8.
    AbdulAzeez, M.A.: Artificial Neural Network Estimation of Global Solar Radiation Using Meteorological Parameters in Gusau, Nigeria. Archives of Applied Science Research 3(2), 586–595 (2011)Google Scholar
  9. 9.
    Mellit, A., Kalogirou, S.A., Hontoria, L., Shaari, S.: Artificial intelligence techniques for sizing photovoltaic systems: a review. Renewable & Sustainable Energy Reviews 13(2), 406–419 (2009)CrossRefGoogle Scholar
  10. 10.
    Zeng, Z., Yang, H., Zhao, R., Meng, J.: Nonlinear characteristics of observed solar radiation data. Solar Energy 87, 204–218 (2013)CrossRefGoogle Scholar
  11. 11.
    Grossberg, S.: Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks 1, 17–61 (1988)CrossRefGoogle Scholar
  12. 12.
    Anderson, J.A.: Introduction to Neural Networks. MIT Press, Cambridge (1995)zbMATHGoogle Scholar
  13. 13.
    Elman, J.: Finding structure in time. Cognitive Sci. 14, 179–211 (1990)CrossRefGoogle Scholar
  14. 14.
    Pearlmutter, B.A.: Gradient calculations for dynamic recurrent neural networks: a survey. IEEE Transactions on Neural Networks 6(5), 1212–1228 (1995)CrossRefGoogle Scholar
  15. 15.
    Hwang, S.Y., Basawa, I.V.: Large sample inference based on multiple observations from nonlinear autoregressive processes. Stochastic Processes and their Applications 49(1), 127–140 (1994)zbMATHMathSciNetCrossRefGoogle Scholar
  16. 16.
    Kapetanios, G.: Nonlinear autoregressive models and long memory. Economics Letters 91(3), 360–368 (2006)zbMATHMathSciNetCrossRefGoogle Scholar
  17. 17.
    Taskaya-Temizel, T., Casey, M.: A comparative study of autoregressive neural network hybrids. Neural Networks 18(5-6), 781–789 (2005)CrossRefGoogle Scholar
  18. 18.
    Guo, W.W., Xue, H.: Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models. Mathematical Problems in Engineering 857865, 7 (2014)MathSciNetGoogle Scholar
  19. 19.
    Kohonen, T.: Self – Organization and Associative Memory. Springer (1989)Google Scholar
  20. 20.
    Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall (1998)Google Scholar
  21. 21.
    Anderson, J.A., Rosenfield, E.: Neurocomputing: Foundations of Research. MIT Press (1989)Google Scholar
  22. 22.
    Giaouris, D., Papadopoulos, A.I., Ziogou, C., Ipsakis, D., Voutetakis, S., Papadopoulou, S., Seferlis, P., Stergiopoulos, F., Elmasides, C.: Performance investigation of a hybrid renewable power generation and storage system using systemic power management models. Energy 61, 621–635 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • P. Chatziagorakis
    • 1
  • C. Elmasides
    • 2
  • G. Ch. Sirakoulis
    • 1
  • I. Karafyllidis
    • 1
  • I. Andreadis
    • 1
  • N. Georgoulas
    • 1
  • D. Giaouris
    • 3
  • A. I. Papadopoulos
    • 3
  • C. Ziogou
    • 3
  • D. Ipsakis
    • 3
  • S. Papadopoulou
    • 3
  • P. Seferlis
    • 3
  • F. Stergiopoulos
    • 3
  • S. Voutetakis
    • 3
  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece
  2. 2.Systems Sunlight S.A.XanthiGreece
  3. 3.Chemical Process Engineering Research Institute, Centre for Research and Technology HellasThermi-ThessalonikiGreece

Personalised recommendations