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Estimating Harvestable Energy in Time-Varying Indoor Light Conditions

Published:16 November 2020Publication History

ABSTRACT

Ambient light energy harvesting is a cost-effective and mature approach for supplying low-power sensor systems with power in many indoor applications. Although the spectral information of a light source is known to influence the efficiency and output power of a photovoltaic cell, the spectrum of the ambient illumination is due to measurement complexity often neglected when characterizing light conditions for power estimation purposes. In this paper we evaluate the influence of considering spectral information on the energy estimation accuracy. We create a dataset of varying light conditions in a typical indoor environment based on eight locations. For each location, we compare the energy estimation accuracy with and without spectral considerations. The results of this investigation demonstrate that a spectrum-based method leads to significant performance improvements in cases where the light condition is not defined by a single light source.

References

  1. Georgia Apostolou and Angèle Reinders. 2014. Overview of design issues in product-integrated photovoltaics. Energy Technology 2, 3 (2014), 229--242. https://doi.org/10.1002/ente.201300158Google ScholarGoogle ScholarCross RefCross Ref
  2. Georgia Apostolou, Angèle Reinders, and Martin Verwaal. 2016. Comparison of the indoor performance of 12 commercial PV products by a simple model. Energy Science and Engineering 4, 1 (2016), 69--85. https://doi.org/10.1002/ese3.110Google ScholarGoogle ScholarCross RefCross Ref
  3. Georgia Apostolou, Martin Verwaal, and Angèle Reinders. 2014. Estimating the performance of product integrated photovoltaic (PIPV) cells under indoor conditions for the support of design processes. In IEEE 40th Photovoltaic Specialist Conference (PVSC). 742--747. https://doi.org/10.1109/PVSC.2014.6925027Google ScholarGoogle ScholarCross RefCross Ref
  4. Stanislav Bobovych, Nilanjan Banerjee, Ryan Robucci, James P. Parkerson, Jackson Schmandt, and Chintan Patel. 2015. SunaPlayer: High-accuracy emulation of solar cells. In Proceedings of the 14th International Conference on Information Processing in Sensor Networks. ACM, 59--70. https://doi.org/10.1145/2737095.2737110 Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Carlos Carvalho and Nuno Paulino. 2014. On the feasibility of indoor light energy harvesting for wireless sensor networks. Procedia Technology 17 (2014), 343--350. https://doi.org/10.1016/j.protcy.2014.10.206Google ScholarGoogle ScholarCross RefCross Ref
  6. Josiah Hester, Timothy Scott, and Jacob Sorber. 2014. Ekho: Realistic and re-peatable experimentation for tiny energy-harvesting sensors. In Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems. ACM, 1--15. https://doi.org/10.1145/2668332.2668336 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Emanuele Lattanzi, Matteo Dromedari, Valerio Freschi, and Alessandro Bogliolo. 2017. Tuning the complexity of photovoltaic array models to meet real-time constraints of embedded energy emulators. Energies 10, 3 (2017). https://doi.org/10.3390/en10030278Google ScholarGoogle Scholar
  8. Y. Li, N.J. Grabham, S.P. Beeby, and M.J. Tudor. 2015. The effect of the type of illumination on the energy harvesting performance of solar cells. Solar Energy 111 (2015), 21--29. https://doi.org/10.1016/j.solener.2014.10.024Google ScholarGoogle ScholarCross RefCross Ref
  9. Xinyu Ma, Sebastian Bader, and Bengt Oelmann. 2017. Characterization of indoor light conditions by light source classification. IEEE Sensors Journal 17, 12 (6 2017), 3884--3891. https://doi.org/10.1109/JSEN.2017.2699330Google ScholarGoogle ScholarCross RefCross Ref
  10. Xinyu Ma, Sebastian Bader, and Bengt Oelmann. 2019. A Scalable, Data-Driven Approach for Power Estimation of Photovoltaic Devices under Indoor Conditions. In Proceedings of the 7th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems. 29--34. https://doi.org/10.1145/3362053.3363494 Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Xinyu Ma, Sebastian Bader, and Bengt Oelmann. 2020. Power Estimation for Indoor Light Energy Harvesting Systems. IEEE Transactions on Instrumentation and Measurement 69, 10 (2020), 7513--7521. https://doi.org/10.1109/TIM.2020.2984145Google ScholarGoogle ScholarCross RefCross Ref
  12. Ian Mathews, Sai Nithin Kantareddy, Tonio Buonassisi, and Ian Marius Peters. 2019. Technology and market perspective for indoor photovoltaic cells. Joule 3, 6 (2019), 1415--1426. https://doi.org/10.1016/j.joule.2019.03.026Google ScholarGoogle ScholarCross RefCross Ref
  13. Ben Minnaert and Peter Veelaert. 2014. A proposal for typical artificial light sources for the characterization of indoor photovoltaic applications. Energies 7, 3 (2014), 1500--1516. https://doi.org/10.3390/en7031500Google ScholarGoogle ScholarCross RefCross Ref
  14. Monika Müller, J. Wienold, William D. Walker, and Leonard M. Reindl. 2009. Characterization of indoor photovoltaic devices and light. In 34th IEEE Photovoltaic Specialists Conference (PVSC). 738--743. https://doi.org/10.1109/PVSC.2009.5411178Google ScholarGoogle Scholar
  15. Adel Nasiri, Salaheddin A. Zabalawi, and Goran Mandic. 2009. Indoor Power Harvesting Using Photovoltaic Cells for Low-Power Applications. IEEE Transactions on Industrial Electronics 56, 11 (2009), 4502--4509.Google ScholarGoogle ScholarCross RefCross Ref
  16. Nurani Saoda and Bradford Campbell. 2019. No Batteries Needed: Providing Physical Context with Energy-Harvesting Beacons. In Proceedings of the 7th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems. 15--21. https://doi.org/10.1145/3362053.3363489 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. John Sarik, Kanghwan Kim, Maria Gorlatova, Ioannis Kymissis, and Gil Zussman. 2013. More than meets the eye: A portable measurement unit for characterizing light energy availability. In IEEE Global Conference on Signal and Information Processing. 387--390. https://doi.org/10.1109/GlobalSIP.2013.6736896Google ScholarGoogle ScholarCross RefCross Ref

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        • Published in

          cover image ACM Conferences
          ENSsys '20: Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems
          November 2020
          91 pages
          ISBN:9781450381291
          DOI:10.1145/3417308

          Copyright © 2020 ACM

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          Publication History

          • Published: 16 November 2020

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