Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System
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 oth...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/19/7222 |
| _version_ | 1797479441558929408 |
|---|---|
| author | Zoltan Varga Ervin Racz |
| author_facet | Zoltan Varga Ervin Racz |
| author_sort | Zoltan Varga |
| collection | DOAJ |
| description | 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<sup>2</sup>, 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<sup>2</sup> to circulate water. |
| first_indexed | 2024-03-09T21:46:43Z |
| format | Article |
| id | doaj.art-529505f536a3455fb97a5d6113fe6263 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:46:43Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-529505f536a3455fb97a5d6113fe62632023-11-23T20:15:12ZengMDPI AGEnergies1996-10732022-10-011519722210.3390/en15197222Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid SystemZoltan Varga0Ervin Racz1Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, 1034 Budapest, HungaryDepartment of Natural Science, Institute of Electrophysics, Kandó Kálmán Faculty of Electrical Engineering, Óbuda University, 1034 Budapest, HungaryIn 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<sup>2</sup>, 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<sup>2</sup> to circulate water.https://www.mdpi.com/1996-1073/15/19/7222dye-sensitized solar cellthermoelectric generatorhybrid solar cellwaste heatdecision tree regressionrandom forest regression |
| spellingShingle | Zoltan Varga Ervin Racz Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System Energies dye-sensitized solar cell thermoelectric generator hybrid solar cell waste heat decision tree regression random forest regression |
| title | Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System |
| title_full | Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System |
| title_fullStr | Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System |
| title_full_unstemmed | Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System |
| title_short | Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System |
| title_sort | machine learning analysis on the performance of dye sensitized solar cell thermoelectric generator hybrid system |
| topic | dye-sensitized solar cell thermoelectric generator hybrid solar cell waste heat decision tree regression random forest regression |
| url | https://www.mdpi.com/1996-1073/15/19/7222 |
| work_keys_str_mv | AT zoltanvarga machinelearninganalysisontheperformanceofdyesensitizedsolarcellthermoelectricgeneratorhybridsystem AT ervinracz machinelearninganalysisontheperformanceofdyesensitizedsolarcellthermoelectricgeneratorhybridsystem |