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Performance Prediction of Hybrid Energy Harvesting Devices Using Machine Learning

Authors
Park, YoonbeomCho, KyoungahKim, Sangsig
Issue Date
9-3월-2022
Publisher
AMER CHEMICAL SOC
Keywords
thermoelectric generator; photovoltaic cell; interface; hybrid energy device; machine learning
Citation
ACS APPLIED MATERIALS & INTERFACES, v.14, no.9, pp.11248 - 11254
Indexed
SCIE
SCOPUS
Journal Title
ACS APPLIED MATERIALS & INTERFACES
Volume
14
Number
9
Start Page
11248
End Page
11254
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/141908
DOI
10.1021/acsami.1c21856
ISSN
1944-8244
Abstract
In this study, we used machine learning to predict the output power of hybrid energy devices (HEDs) comprising photovoltaic cells (PVCs) and thermoelectric generators (TEGs). For the five types of HEDs, eight different machine learning models were trained and tested with experimental data; the HED each had different interface materials between the PVCs and the TEGs. An artificial neural network (ANN) model, which is the most appropriate model, predicted the correlation between HED performance and interface material properties. The ANN model demonstrated that the output power of the HED with a carbon paste interface material at an irradiance of 1000 W/m(2) was 2.6% higher than that of a PVC alone.
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