Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System

Zoltan Varga () and Ervin Racz
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Zoltan Varga: Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, 1034 Budapest, Hungary
Ervin Racz: Department of Natural Science, Institute of Electrophysics, Kandó Kálmán Faculty of Electrical Engineering, Óbuda University, 1034 Budapest, Hungary

Energies, 2022, vol. 15, issue 19, 1-18

Abstract: In cases where a dye-sensitized solar cell (DSSC) is exposed to light, thermal energy accumulates inside the device, reducing the maximum power output. Utilizing this energy via the Seebeck effect can convert thermal energy into electrical current. Similar systems have been designed and built by other researchers, but associated tests were undertaken in laboratory environments using simulated sunlight and not outdoor conditions with methods that belong to conventional data analysis and simulation methods. In this study four machine learning techniques were analyzed: decision tree regression (DTR), random forest regression (RFR), K-nearest neighbors regression (K-NNR), and artificial neural network (ANN). DTR algorithm has the least errors and the most R 2 , indicating it as the most accurate method. The DSSC-TEG hybrid system was extrapolated based on the results of the DTR and taking the worst-case scenario (node-6). The main question is how many thermoelectric generators (TEGs) are needed for an inverter to operate a hydraulic pump to circulate water, and how much area is required for that number of TEGs. Considering the average value of the electric voltage of the TEG belonging to node-6, 60,741 pieces of TEGs would be needed, which means about 98 m 2 to circulate water.

Keywords: dye-sensitized solar cell; thermoelectric generator; hybrid solar cell; waste heat; decision tree regression; random forest regression; k-nearest neighbors regression; artificial neural network; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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