Araştırma Makalesi
Yıl 2023,
Cilt: 44 Sayı: 4, 768 - 774, 28.12.2023
Öz
CdTe solar cells on ultra-thin glass substrates are light and flexible. Flexible cells are widely preferred modules in technological fields. The flexibility of these cells enables them to cope with deformations. The efficiency of these has reached 19%. In this work, we used artificial neural network (ANN) method for the determination the performance of flexible CdTe solar cells despite bending and time. The performances of the solar cell before and after bending have been predicted. According to the results from the ANN calculations using the experimental data in the literature, MSE values of ANN estimates range from 0.06% to 0.28%.
Anahtar Kelimeler
CdTe flexible solar cell Solar cell efficiency Artificial neural network
Kaynakça
- [1] Romeo A., Artegiani E., Cdte-based thin film solar cells: Past, present and future, Energies, 14 (2021) 1684.
- [2] Fang, Z., Wang X.C., Wu H.C., Zhao, C.Z., Achievements and challenges of CdS/CdTe solar cells, International Journal of Photoenergy, (2011) 297350.
- [3] Mathew X., Thompson G.W., Singh V.P., McClure J.C., Velumani S., Mathews N.R., Sebastian P.J., Development of CdTe thin films on flexible substrates—a review, Solar Energy Materials and Solar Cells, 76 (2003) 293.
- [4] Kranz L., Buecheler S., Tiwari A.N., Technological status of CdTe photovoltaics, Solar Energy Materials and Solar Cells, 119 (2013) 278.
- [5] Teloeken A.C., Lamb D.A., Dunlop T.O., Irvine S.J.C., Effect of bending test on the performance of CdTe solar cells on flexible ultra-thin glass produced by MOCVD, Solar Energy Materials and Solar Cells, 211 (2020) 110552.
- [6] Qian S., Deng Y., Li X., Jin, Z., Long E., Prediction and influence of the mass proportion of trichromatic colourants and acrylic substrate on the optical and thermal performance of external wall coatings: An artificial neural network approach, Solar Energy Materials and Solar Cells, 236 (2022) 111551.
- [7] Wang S., Zhanga Y., Hao P.,Lu H., An improved method for PV output prediction using artificial neural network with overlap training range, Journal of Renewable and Sustainable Energy, 13 (2021) 063502.
- [8] Su M., Yang Ji-H., Liu, Zhi-P., Gong Xin-G., Exploring Large-Lattice-Mismatched Interfaces with Neural Network Potentials: The Case of the CdS/CdTe Heterostructure, J. Phys. Chem. C 126 (2022) 13366.
- [9] Jaber M., Abd Hamid A.S., Sopian K., Fazliza, A., Ibrahim A., Prediction Model for the Performance of Different PV Modules Using Artificial Neural Networks, Appl. Sci. 12 (2022) 3349.
- [10] Haykin S., “Neural Networks: a Comprehensive Foundation” Englewood Cliffs, Prentice-Hall, New Jersey, pp.842 (1999).
- [11] Hornik K., Stinchcombe M. and White H., Neural Networks, 2 (1989) 359.
- [12] Levenberg K., Amethodforthesolutionofcertainnon-linearproblems inleastsquares, Q. Appl. Math., 2 (1944) 164.
- [13] Marquardt D., An algorithm for least-squares estimation of nonlinear parameters, SIAM J. Appl. Math., 11 (1963) 431.
Yıl 2023,
Cilt: 44 Sayı: 4, 768 - 774, 28.12.2023
Öz
Kaynakça
- [1] Romeo A., Artegiani E., Cdte-based thin film solar cells: Past, present and future, Energies, 14 (2021) 1684.
- [2] Fang, Z., Wang X.C., Wu H.C., Zhao, C.Z., Achievements and challenges of CdS/CdTe solar cells, International Journal of Photoenergy, (2011) 297350.
- [3] Mathew X., Thompson G.W., Singh V.P., McClure J.C., Velumani S., Mathews N.R., Sebastian P.J., Development of CdTe thin films on flexible substrates—a review, Solar Energy Materials and Solar Cells, 76 (2003) 293.
- [4] Kranz L., Buecheler S., Tiwari A.N., Technological status of CdTe photovoltaics, Solar Energy Materials and Solar Cells, 119 (2013) 278.
- [5] Teloeken A.C., Lamb D.A., Dunlop T.O., Irvine S.J.C., Effect of bending test on the performance of CdTe solar cells on flexible ultra-thin glass produced by MOCVD, Solar Energy Materials and Solar Cells, 211 (2020) 110552.
- [6] Qian S., Deng Y., Li X., Jin, Z., Long E., Prediction and influence of the mass proportion of trichromatic colourants and acrylic substrate on the optical and thermal performance of external wall coatings: An artificial neural network approach, Solar Energy Materials and Solar Cells, 236 (2022) 111551.
- [7] Wang S., Zhanga Y., Hao P.,Lu H., An improved method for PV output prediction using artificial neural network with overlap training range, Journal of Renewable and Sustainable Energy, 13 (2021) 063502.
- [8] Su M., Yang Ji-H., Liu, Zhi-P., Gong Xin-G., Exploring Large-Lattice-Mismatched Interfaces with Neural Network Potentials: The Case of the CdS/CdTe Heterostructure, J. Phys. Chem. C 126 (2022) 13366.
- [9] Jaber M., Abd Hamid A.S., Sopian K., Fazliza, A., Ibrahim A., Prediction Model for the Performance of Different PV Modules Using Artificial Neural Networks, Appl. Sci. 12 (2022) 3349.
- [10] Haykin S., “Neural Networks: a Comprehensive Foundation” Englewood Cliffs, Prentice-Hall, New Jersey, pp.842 (1999).
- [11] Hornik K., Stinchcombe M. and White H., Neural Networks, 2 (1989) 359.
- [12] Levenberg K., Amethodforthesolutionofcertainnon-linearproblems inleastsquares, Q. Appl. Math., 2 (1944) 164.
- [13] Marquardt D., An algorithm for least-squares estimation of nonlinear parameters, SIAM J. Appl. Math., 11 (1963) 431.
Toplam 13 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Atomik, Moleküler ve Optik Fizik (Diğer) |
| Bölüm | Natural Sciences |
| Yazarlar | |
| Yayımlanma Tarihi | 28 Aralık 2023 |
| Gönderilme Tarihi | 9 Haziran 2023 |
| Kabul Tarihi | 2 Ekim 2023 |
| Yayımlandığı Sayı | Yıl 2023Cilt: 44 Sayı: 4 |
