Segmented thermoelectric generator modelling and optimization using artificial neural networks by iterative training

Abstract

Renewable energy technologies are central to emissions reduction and essential to achieve net-zero emission. Segmented thermoelectric generators (STEG) facilitate more efficient thermal energy recovery over a large temperature gradient. However, the additional design complexity has introduced challenges in the modelling and optimization of its performance. In this work, an artificial neural network (ANN) has been applied to build accurate and fast forward modelling of the STEG. More importantly, we adopt an iterative method in the ANN training process to improve accuracy without increasing the dataset size. This approach strengthens the pro- portion of the high-power performance in the STEG training dataset. Without increasing the size of the training dataset, the relative prediction error over high-power STEG designs decreases from 0.06 to 0.02, representing a threefold improvement. Coupling with a genetic algorithm, the trained artificial neural networks can perform design optimization within 10 s for each operating condition. It is over 5,000 times faster than the optimization performed by the conventional finite element method. Such an accurate and fast modeller also allows mapping of the STEG power against different parameters. The modelling approach demonstrated in this work indicates its future application in designing and optimizing complex energy harvesting technologies.

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Identifiers

ORCID for Daniel W Newbrook: orcid.org/0000-0002-5047-6168

ORCID for Peng Dai: orcid.org/0000-0002-5973-9155

ORCID for C.H. Kees De Groot: orcid.org/0000-0002-3850-7101

ORCID for Ruomeng Huang: orcid.org/0000-0003-1185-635X

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