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Characterization of Indoor Light Conditions by Light Source Classification
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.ORCID iD: 0000-0002-8382-0359
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2017 (English)In: IEEE Sensors Journal, ISSN 1530-437X, E-ISSN 1558-1748, Vol. 17, no 12, 3884-3891 p., 7914682Article in journal (Refereed) Published
Abstract [en]

The characterization of light conditions plays an important role in the estimation of available energy levels to ambient light energy harvesting systems. Indoor light conditions are commonly described by illuminance levels. The same illuminance levels, however, can be generated by different light source types, which radiate different spectral components. This means that based on their spectral response, solar panels can produce different output powers even though identical illuminance levels are observed. We propose a method to distinguish these conditions based on limited spectral information. Using low-cost sensors, spectral characteristics of the light condition can be acquired and used to classify the underlying light source type, which allows for a more accurate estimation of the solar panel response. The method was evaluated experimentally for a number of common indoor light sources and under different conditions. Evaluation results have shown that the method can be used to distinguish the light sources under test with very high classification accuracy. Moreover, the method can be used accurately in situations with limited interference. This makes it a low-cost alternative to the characterization of light conditions using spectrometers, the use of which is infeasible in spatially distributed characterization applications.

Place, publisher, year, edition, pages
2017. Vol. 17, no 12, 3884-3891 p., 7914682
Keyword [en]
Energy harvesting, sensor systems, ambient light characterization, classification algorithms
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-31344DOI: 10.1109/JSEN.2017.2699330ISI: 000402123400032Scopus ID: 2-s2.0-85021728867OAI: oai:DiVA.org:miun-31344DiVA: diva2:1130085
Available from: 2017-08-08 Created: 2017-08-08 Last updated: 2017-08-11Bibliographically approved

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Ma, XinyuBader, SebastianOelmann, Bengt
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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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