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).
[PDF]
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.
