TR2018-077

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

    •  Sanz, I., Bernal, C., Bono, A., Pajovic, M., Martinez, G., "Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems", Jornada de Jovenes Investigadores, July 11, 2018.
      BibTeX TR2018-077 PDF
      • @inproceedings{Sanz2018jul,
      • author = {Sanz, Ivan and Bernal, Carlos and Bono, Antonio and Pajovic, Milutin and Martinez, Gabriel},
      • title = {Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems},
      • booktitle = {Jornada de Jovenes Investigadores},
      • year = 2018,
      • number = 6,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2018-077}
      • }
  • Research Areas:

    Artificial Intelligence, Machine Learning, Signal Processing

Abstract:

This study addresses the use of GPR techniques for VRLA battery voltage prediction purposes in PV offgrid 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.