Prediction and analysis of energy generation from thermoelectric energy generator with operating environmental parameters

Zi Yang Adrian Ang, Wai Lok Woo, Ehsan Mesbahi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents an integrated artificial neural network (ANN) approach for the design and prediction of energy generated from a thermoelectric generator (TEG) under the influence of the operating environmental parameters. The unique ANN model can predict the output voltage generated as well as ensuring the reliability of the output. Deriving of individual input parameter can also be obtained from the learned network when given a required output voltage together with sensitivity analysis for the identification of key input parameters that have strong influence in the output value generated by the TEG were also incorporated in the model, making it an efficient tool for the design and conceptualisation of a TEG. This proposed approach is particularly useful when TEG users are faced with limited resources for achieving their required output power. The developed ANN model shows the mean square error (MSE) of 0.0008 in modelling an experimental dataset consisting of 4096 data. The predicted results obtained from the optimized ANN model are also verified with the testing of experimental data and a good agreement is obtained with errors of +/- 0.15.
Original languageEnglish
Title of host publicationInternational Conference on Green Energy and Applications (ICGEA)
PublisherIEEE
Pages80-84
Number of pages5
ISBN (Electronic)978-1-5386-3985-6
ISBN (Print)978-1-5386-3986-3, 978-1-5386-3983-2
DOIs
Publication statusPublished - 11 May 2017

Keywords

  • energy harvesting
  • thermoelectricity
  • renewable energy
  • artificial neural network

Cite this

Ang, Z. Y. A., Woo, W. L., & Mesbahi, E. (2017). Prediction and analysis of energy generation from thermoelectric energy generator with operating environmental parameters. In International Conference on Green Energy and Applications (ICGEA) (pp. 80-84). IEEE. https://doi.org/10.1109/ICGEA.2017.7925459