General Information
    • ISSN: 1793-821X
    • Frequency: Quarterly (2013-2014); Bimonthly (Since 2015)
    • DOI: 10.18178/JOCET
    • Editor-in-Chief: Prof. Haider F. Abdul Amir
    • Executive Editor: Ms. Julia S. Ma
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Editor-in-chief
School of Science and Technology Universiti Malaysia Sabah, Malaysia.
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JOCET 2017 Vol.5(6): 458-463 ISSN: 1793-821X
DOI: 10.18178/JOCET.2017.5.6.416

Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters

Zi Yang Adrian Ang, Wai Lok Woo, and Ehsan Mesbahi
Abstract—This paper focus on a new methodology approach to evaluate more accurately the energy generated from Thermoelectric Generator (TEG) under the influence of its operating environmental parameters. An artificial neural network (ANN) model for predicting the energy generated by a TEG in its operating environment has been developed. The dataset generated through a validated finite volume method is trained in a supervised way and tested by a multi-layer perceptron (MLP) to predict the energy generated. However, the degree of ambiguity may vary widely across the whole range of input values therefore in this paper, a new methodological approach will be incorporated to not only predict the average value but as well as evaluating the reliability of the output value with the use of a scheme which is made up of two coupled neural network. Apart from predicting the output values, this model can perform reverse ANN to predict the input value when provided with an output value.

Index Terms—Artificial neural network, energy, heat transfer, thermoelectric.

Zi Yang Adrian Ang and Wai Lok Woo are with the School of Electrical and Electronics Engineering, Newcastle University, UK (e-mail: z.y.ang@ncl.ac.uk, lok.woo@ncl.ac.uk).
Ehsan Mesbahi is with University of the West of Scotland, UK (e-mail: ehsan.mesbahi@uws.ac.uk).

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Cite:Zi Yang Adrian Ang, Wai Lok Woo, and Ehsan Mesbahi, "Artificial Neural Network Based Prediction of Energy Generation from Thermoelectric Generator with Environmental Parameters," Journal of Clean Energy Technologies vol. 5, no. 6, pp. 458-463, 2017.

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