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Low-Cost Sensors for Indoor PV Energy Harvesting Estimation Based on Machine Learning

Author

Listed:
  • Bastien Politi

    (Institute of Electronics and Systems, University of Montpellier, 34095 Montpellier, France
    Bureaux A Partager SAS, 75003 Paris, France
    Current address: Hubert Curien Laboratory, University Jean Monnet, 42000 Saint-Etienne, France.)

  • Alain Foucaran

    (Institute of Electronics and Systems, University of Montpellier, 34095 Montpellier, France)

  • Nicolas Camara

    (Institute of Electronics and Systems, University of Montpellier, 34095 Montpellier, France
    EPF—Graduate School of Engineering, 34000 Montpellier, France)

Abstract

With the number of communicating sensors linked to the Internet of Things (IoT) ecosystem increasing dramatically, well-designed indoor light energy harvesting solutions are needed. A first step in this direction would be to be able to accurately estimate the harvestable energy in a specific light environment. However, inside, this energy varies in spectral composition and intensity, depending on the emission source as well as the time of day. These challenging conditions mean that it has become necessary to obtain accurate information about these variations and determine their impact on energy recovery performance. In this context, this manuscript presented a method to apply an innovative energy harvesting estimation method to obtain practical and accurate insight for the design of energy harvesting systems in indoor environments. It used a very low-cost device to obtain spectral information and fed it to supervised machine learning classification methods to recognize light sources. From the recognized light source, a model developed for flexible GaAs solar cells was able to estimate the harvestable energy. To validate this method in real indoor conditions, the estimates were compared to the energy harvested by an energy harvesting prototype. The mean absolute error percentage between estimates and the experimental measurements was less than 5% after more than 2 weeks of observation. This demonstrated the potential of this low-cost estimation system to obtain reliable information to design energetically autonomous devices.

Suggested Citation

  • Bastien Politi & Alain Foucaran & Nicolas Camara, 2022. "Low-Cost Sensors for Indoor PV Energy Harvesting Estimation Based on Machine Learning," Energies, MDPI, vol. 15(3), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1144-:d:741742
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    References listed on IDEAS

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    1. Ben Minnaert & Peter Veelaert, 2014. "A Proposal for Typical Artificial Light Sources for the Characterization of Indoor Photovoltaic Applications," Energies, MDPI, vol. 7(3), pages 1-17, March.
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    Cited by:

    1. Insu Kim & Beopsoo Kim & Denis Sidorov, 2022. "Machine Learning for Energy Systems Optimization," Energies, MDPI, vol. 15(11), pages 1-8, June.
    2. Mitsuhide Sato & Takuto Takemura & Tsutomu Mizuno, 2022. "Voltage Improvement of a Swing-Magnet-Type Generator for Harvesting Bicycle Vibrations," Energies, MDPI, vol. 15(13), pages 1-14, June.

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