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Neural Computing and Applications

, Volume 27, Issue 5, pp 1093–1118 | Cite as

Enhancement of hybrid renewable energy systems control with neural networks applied to weather forecasting: the case of Olvio

  • P. Chatziagorakis
  • C. Ziogou
  • C. Elmasides
  • G. Ch. Sirakoulis
  • I. Karafyllidis
  • I. Andreadis
  • N. Georgoulas
  • D. Giaouris
  • A. I. Papadopoulos
  • D. Ipsakis
  • S. Papadopoulou
  • P. Seferlis
  • F. Stergiopoulos
  • S. Voutetakis
EANN
  • 292 Downloads

Abstract

In this paper, an intelligent forecasting model, a recurrent neural network (RNN) with nonlinear autoregressive architecture, for daily and hourly solar radiation and wind speed prediction is proposed for the enhancement of the power management strategies (PMSs) of hybrid renewable energy systems (HYRES). The presented model (RNN) is applicable to an autonomous HYRES, where its estimations can be used by a central control unit in order to create in real time the proper PMSs for the efficient subsystems’ utilization and overall process optimization. For this purpose, 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. The simulation results indicated that RNN is capable of assimilating the given information and delivering some satisfactory future estimation achieving regression coefficient from 0.93 up to 0.99 that can be used to safely calculate the available green energy. Moreover, it has some sufficient for the specific problem computational power, as it can deliver the final results in just a few seconds. As a result, the RNN framework, trained with local meteorological data, successfully manages to enhance and optimize the PMS based on the provided solar radiation and wind speed prediction and make the specific HYRES suitable for use as a stand-alone remote energy plant.

Keywords

Recurrent neural network Solar radiation Power management strategy Hybrid renewable energy system 

Notes

Acknowledgments

This work is co-financed by National Strategic Reference Framework (NSRF) 2007–2013 of Greece and the European Union Research Program “SYNERGASIA” (SUPERMICRO – 09ΣYN-32-594).

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • P. Chatziagorakis
    • 1
  • C. Ziogou
    • 4
  • C. Elmasides
    • 2
    • 3
  • G. Ch. Sirakoulis
    • 1
  • I. Karafyllidis
    • 1
  • I. Andreadis
    • 1
  • N. Georgoulas
    • 1
  • D. Giaouris
    • 4
  • A. I. Papadopoulos
    • 4
  • D. Ipsakis
    • 4
  • S. Papadopoulou
    • 4
  • P. Seferlis
    • 4
    • 5
  • F. Stergiopoulos
    • 4
  • S. Voutetakis
    • 4
  1. 1.Department of Electrical and Computer EngineeringDemocritus University of ThraceXanthiGreece
  2. 2.Department of Environmental EngineeringDemocritus University of ThraceXanthiGreece
  3. 3.Systems Sunlight S.A.XanthiGreece
  4. 4.Chemical Process and Energy Resources InstituteCentre for Research and Technology – HellasThermi, ThessalonikiGreece
  5. 5.Department of Mechanical EngineeringAristotle University of ThessalonikiThessalonikiGreece

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