Submitted:
22 July 2025
Posted:
29 July 2025
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Abstract
Keywords:
1. Introduction
2. Experimental Location
3. Environmental Stressors
4. Implications for Performance
5. Results and Discussion
6. Conclusions
Acknowledgments
References
- A. Bouaichi, P.-O. Logerais, A. El Amrani, A. Ennaoui, and C. Messaoudi, “Comprehensive analysis of aging mechanisms and design solutions for desert-resilient photovoltaic modules,” Sol. Energy Mater. Sol. Cells, vol. 267, p. 112707, 2024. [CrossRef]
- N. Kahoul, R. Chenni, H. Cheghib, and S. Mekhilef, “Evaluat- ing the reliability of crystalline silicon photovoltaic modules in harsh environment,” Renew. Energy, vol. 109, pp. 66–72, 2017. [CrossRef]
- H. Al Mahdi, P. G. Leahy, M. Alghoul, and A. P. Morrison, “A Review of Photovoltaic Module Failure and Degradation Mechanisms: Causes and Detection Techniques,” 2024. [CrossRef]
- H. Wang et al., “Potential-induced degradation: Recombination behav- ior, temperature coefficients and mismatch losses in crystalline silicon photovoltaic power plant,” Sol. Energy, vol. 188, pp. 258–264, 2019. [CrossRef]
- M. Va´zquez and I. Rey-Stolle, “Photovoltaic module reliability model based on field degradation studies,” Prog. Photovoltaics Res. Appl., vol. 16, no. 5, pp. 419–433, Aug. 2008. [CrossRef]
- B. Al-Ramadan, A. S. Aldosary, A. Al Kafy, S. Alsulamy, and Z. A. Rahaman, “Unraveling the spatiotemporal dynamics of relative humidity in major Saudi Arabian cities: A synergy of climate modeling, regression analysis, and wavelet coherence,” Theor. Appl. Climatol., vol. 155, no. 8, pp. 7909–7935, 2024. [CrossRef]
- A. Qudah, A. Almerbati, and E. M. A. Mokheimer, “Novel approach for optimizing wind-PV hybrid system for RO desalination using differential evolution algorithm,” Energy Convers. Manag., vol. 300, p. 117949, 2024. [CrossRef]
- S. Poddar, F. Rougieux, J. P. Evans, M. Kay, A. A. Prasad, and S. P. Bremner, “Accelerated degradation of photovoltaic modules under a future warmer climate,” Prog. Photovoltaics Res. Appl., vol. 32, no. 7, pp. 456–467, Jul. 2024. [CrossRef]
- Y. Lyu et al., “Impact of environmental variables on the degradation of photovoltaic components and perspectives for the reliability assess- ment methodology,” Sol. Energy, vol. 199, pp. 425–436. 2020. [CrossRef]
- B. Bora et al., “Accelerated stress testing of potential induced degradation susceptibility of PV modules under different cli- matic conditions,” Sol. Energy, vol. 223, pp. 158–167. 2021. [CrossRef]
- A. Pozza and T. Sample, “Crystalline silicon PV module degradation after 20years of field exposure studied by electrical tests, electrolumi- nescence, and LBIC,” Prog. Photovoltaics Res. Appl., vol. 24, no. 3, pp. 368–378, Mar. 2016. [CrossRef]
- V. E. Puranik, R. Kumar, and R. Gupta, “Progress in module level quantitative electroluminescence imaging of crystalline silicon PV module: A review,” Sol. Energy, vol. 264, p. 111994, 2023. [CrossRef]
- R. Khatri, S. Agarwal, I. Saha, S. K. Singh, and B. Kumar, “Study on long term reliability of photo-voltaic modules and analysis of power degradation using accelerated aging tests and electrolumines- cence technique,” Energy Procedia, vol. 8, pp. 396–401, 2011. [CrossRef]





| Parameter | Manufacturer Data |
Lab Test Data After 13 Years |
Decrease in % |
| Pmp [W] | 100 W | 70.39 | 29.61 |
| Isc (A) | 5.68 | 5.36 | 5.63 |
| Voc (V) | 22.8 | 22.31 | 2.14 |
| Vmp [V] | 19 | 16.06 | 15.47 |
| Imp [A] | 5.26 | 4.38 | 16.76 |
| (FF) | – | 58.86% | – |
| Year |
13-Year Used PV Module Power (Watts) |
Forecast 13-Year Used PV Module Power (Watts) |
Lower Confidence Bound (Watts) |
Upper Confidence Bound (Watts) |
| 0 | 100.00 | |||
| 1 | 97.72 | |||
| 2 | 95.49 | |||
| 3 | 93.31 | |||
| 4 | 91.19 | |||
| 5 | 89.11 | |||
| 6 | 87.08 | |||
| 7 | 85.09 | |||
| 8 | 83.15 | |||
| 9 | 81.26 | |||
| 10 | 79.40 | |||
| 11 | 77.59 | |||
| 12 | 75.82 | |||
| 13 | 74.09 | 74.09 | 74.09 | 74.09 |
| 14 | 72.32 | 72.32 | 72.04 | 72.60 |
| 15 | 70.59 | 70.59 | 70.20 | 70.98 |
| 16 | 68.86 | 68.86 | 68.29 | 69.43 |
| 17 | 67.13 | 67.13 | 66.33 | 67.93 |
| 18 | 65.40 | 65.40 | 64.34 | 66.46 |
| 19 | 63.67 | 63.67 | 62.32 | 65.02 |
| 20 | 61.94 | 61.94 | 60.28 | 63.60 |
| 21 | 60.21 | 60.21 | 58.21 | 62.21 |
| 22 | 58.48 | 58.48 | 56.12 | 60.83 |
| 23 | 56.75 | 56.75 | 54.02 | 59.48 |
| 24 | 55.02 | 55.02 | 51.89 | 58.15 |
| 25 | 53.29 | 53.29 | 49.74 | 56.83 |
| Statistic | Value |
| Alpha | 0.50 |
| Beta | 0.50 |
| Gamma | 0.00 |
| MASE | 0.04 |
| SMAPE | 0.00 |
| MAE | 0.08 |
| RMSE | 0.08 |
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