TR2018-077
Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems
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- "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}
- }
,
- "Gaussian Process Regression Applied to VRLA Battery Voltage Prediction in Photovoltaic Off-Grid Systems", Jornada de Jovenes Investigadores, July 11, 2018.
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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.