4.8 Article

Performance Prediction of Hybrid Energy Harvesting Devices Using Machine Learning

期刊

ACS APPLIED MATERIALS & INTERFACES
卷 14, 期 9, 页码 11248-11254

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acsami.1c21856

关键词

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

资金

  1. National Research Foundation of Korea - Ministry of Science, ICT, and Future Planning [NRF2017M1A2A2087323]
  2. National Research Foundation of Korea (NRF) - Korean government (MSIT) [NRF-2020R1A2C3004538]
  3. Brain Korea 21 Plus Project in 2021
  4. Korea University Grant

向作者/读者索取更多资源

This study used machine learning to predict the output power of hybrid energy devices (HEDs) consisting of photovoltaic cells (PVCs) and thermoelectric generators (TEGs). It found that different interface materials have an impact on the HED performance, and a carbon paste interface material can increase the output power by 2.6%.
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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