Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems

Authors

  • Iván Sanz Gorrachategui , Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza
  • Carlos Bernal Ruiz , Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza
  • Antonio Bono Nuez , Human OpenWare Research Lab (HOWLab), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza
  • Milutin Pajovic , Mitsubishi Electric Research Laboratory (MERL), Boston
  • Gabriel Martínez Ruata , Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza

DOI:

https://doi.org/10.26754/jji-i3a.201802826

Abstract

This study addresses the use of GPR techniques for VRLA battery voltage prediction purposes in PV off-grid systems. The goal is to know whether the system is able to endure a predictable power consumption pattern without running out of energy. Two approaches are considered: sample based prediction and pattern-based forecasting.

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Author Biographies

  • Carlos Bernal Ruiz, , Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza

     

     
  • Antonio Bono Nuez, , Human OpenWare Research Lab (HOWLab), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza

     

     
  • Milutin Pajovic, , Mitsubishi Electric Research Laboratory (MERL), Boston

     

     
  • Gabriel Martínez Ruata, , Group of Power Electronics and Microelectronics (GEPM), Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza

     

     

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Published

2018-05-25

How to Cite

Sanz Gorrachategui, I., Bernal Ruiz, C., Bono Nuez, A., Pajovic, M., & Martínez Ruata, G. (2018). Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems. Jornada De Jóvenes Investigadores Del I3A, 6. https://doi.org/10.26754/jji-i3a.201802826