Performance Prediction of Hybrid Energy Harvesting Devices Using Machine Learning

Yoonbeom Park, Kyoungah Cho, Sangsig Kim

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


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/m2 was 2.6% higher than that of a PVC alone.

Original languageEnglish
Pages (from-to)11248-11254
Number of pages7
JournalACS Applied Materials and Interfaces
Issue number9
Publication statusPublished - 2022 Mar 9

Bibliographical note

Funding Information:
This study was supported in part by the Technology Development Program to Solve Climate Change (NRF-2017M1A2A2087323) through the National Research Foundation of Korea funded by the Ministry of Science, ICT, and Future Planning. This study was also supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT; NRF-2020R1A2C3004538), Brain Korea 21 Plus Project in 2021, and the Korea University Grant.

Publisher Copyright:
© 2022 American Chemical Society


  • hybrid energy device
  • interface
  • machine learning
  • photovoltaic cell
  • thermoelectric generator

ASJC Scopus subject areas

  • General Materials Science


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