Concentrated Photovoltaic Thermoelectric Hybrid System: An Experimental and Machine Learning Study
Zeming He, Ming Yang, Lei Wang, Ergude Bao and Hang Zhang
1 Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, 100190, China
2 University of Chinese Academy of Sciences, Beijing, 100049, China
3 School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China
# These authors contributed to this work equally.
Abstract
Applying solar energy over a wider spectral range can lead to more efficient energy conversion. The combination of a photovoltaic (PV) cell and a thermoelectric generator (TEG) is a widely studied technology for effectively broadening the use of the solar spectrum. In this paper, we select two kinds of photovoltaic cells and combine them with a TEG to form different systems, and analyze the overall performance of each system to provide a certain reference for optimal use of photovoltaic cells and a TEG in a hybrid system. Furthermore, we use machine learning to optimize the structural parameters of the hybrid system, and predict the optimal output power of the system when the area ratio of the TEG and PV module is 4.41. This work provides an important reference for further research on the PV-TEG hybrid system and its applications.
Publication details
Received: 25 Feb 2021
Revised: 08 Mar 2021
Accepted: 17 Mar 2021
Published online: 17 Mar 2021
Article type:
Research Paper
DOI:
10.30919/es8d440
Volume:
15
Page:
47-56
Citation:
Engineered Science, 2021, 15, 47-56
Permissions:
Copyright
Number of downloads:
3945
Citation Information:
15
Description:
We use machine learning to optimize the structural parameters of the PV-TEG hybrid system, and predi....
We use machine learning to optimize the structural parameters of the PV-TEG hybrid system, and predict the system performance.
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