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Configuring artificial neural networks for the prediction of available energy in solar-powered sensor nodes
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.
2015 (English)In: 2015 IEEE SENSORS - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2015, 354-357 p., 7370253Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

The behavior prediction of solar energy harvesting systems requires accurate system models in order to dimension the system with respect to its application and location constraints. In contrast to commonly used equivalent circuit models, artificial neural networks (ANN) allow for the behavior of the entire system to be captured in an efficient manner. In this work, we have investigated the influences of the underlying ANN configuration on the model's prediction performance. It was found that the performance variation between training runs increases with an rising number of neurons, which can lead to a higher model performance, but makes the performance outcome more sensitive to initial training conditions. Moreover, the results demonstrate that even simple ANN configurations capture the system behavior accurately and result in low prediction errors for the harvesting architecture under test.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 354-357 p., 7370253
Keyword [en]
artificial neural networks, energy harvesting, solar energy harvesting, system modeling, wireless sensor networks
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-27847DOI: 10.1109/ICSENS.2015.7370253ISI: 000380440800092Scopus ID: 2-s2.0-84963555127Local ID: STCOAI: oai:DiVA.org:miun-27847DiVA: diva2:934751
Conference
14th IEEE SENSORS; Busan; South Korea; 1 November 2015 through 4 November 2015; Category numberCFP15SEN-USB; Code 118927
Note

Conference Paper

Available from: 2016-06-09 Created: 2016-06-09 Last updated: 2016-12-23Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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