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All issues Volume 14 (2023) EPJ Photovolt., 14 (2023) 30 References
Issue
EPJ Photovolt.
Volume 14, 2023
Special Issue on ‘EU PVSEC 2023: State of the Art and Developments in Photovoltaics’, edited by Robert Kenny and João Serra
Article Number 30
Number of page(s) 10
Section Modelling
DOI https://doi.org/10.1051/epjpv/2023018
Published online 23 October 2023
  1. DNV, Energy transition outlook (2022). Available at: https://www.dnv.com/energy-transition-outlook/download.html, visited on 14/09/2023 [Google Scholar]
  2. J. Kim, M. Rabelo, S.P. Padi, H. Yousuf, E.-C. Cho, J. Yi, A review of the degradation of photovoltaic modules for life expectancy, Energies 14, 4278 (2021) [CrossRef] [Google Scholar]
  3. T. Ishii, A. Masuda, Annual degradation rates of recent crystalline silicon photovoltaic modules, Progr. Photovolt.: Res. Appl. 25, 953 (2017) [CrossRef] [Google Scholar]
  4. R. Eke, H. Demircan, Performance analysis of a multi crystalline Si photovoltaic module under Mugla climatic conditions in Turkey, Energ. Conver. Manage. 65, 580 (2013) [CrossRef] [Google Scholar]
  5. V. Sharma, A. Kumar, O. Sastry, S. Chandel, Performance assessment of different solar photovoltaic technologies under similar outdoor conditions, Energy 58, 511 (2013) [CrossRef] [Google Scholar]
  6. B. Marion, M.G. Deceglie, T.J. Silverman, Analysis of measured photovoltaic module performance for Florida, Oregon, and Colorado locations, Sol. Energy 110, 736 (2014) [Google Scholar]
  7. M. Schweiger, J. Bonilla, W. Herrmann, A. Gerber, U. Rau, Performance stability of photovoltaic modules in different climates, Progr. Photovolt.: Res. Appl. 25, 968 (2017) [CrossRef] [Google Scholar]
  8. P. Rajput, G. Tiwari, O. Sastry, B. Bora, V. Sharma, Degradation of mono-crystalline photovoltaic modules after 22 yr of outdoor exposure in the composite climate of India, Sol. Energy 135, 786 (2016) [CrossRef] [Google Scholar]
  9. D.C. Jordan, T.J. Silverman, B. Sekulic, S.R. Kurtz, PV degradation curves: non-linearities and failure modes, Prog. Photovolt. Res. Appl. 25, 583 (2017) [CrossRef] [Google Scholar]
  10. D.C. Jordan, S.R. Kurtz, Photovoltaic degradation rates − an analytical review, Progr. Photovolt.: Res. Appl. 21, 12 (2013) [CrossRef] [Google Scholar]
  11. C. Deline, R. White, M. Muller, K. Anderson, K. Perry, M. Deceglie, L. Simpson, D. Jordan, PV fleet performance data initiative program and methodology, in 47th IEEE Photovoltaic Specialists Conference (PVSC), 2020, pp. 1363–1367 [CrossRef] [Google Scholar]
  12. F. Carigiet, C.J. Brabec, F.P. Baumgartner, Long-term power degradation analysis of crystalline silicon PV modules using indoor and outdoor measurement techniques, Renew. Sust. Energ. Rev. 144, 111005 (2021) [CrossRef] [Google Scholar]
  13. M. Bolinger, W. Gorman, D. Millstein, D. Dirk, System-level performance and degradation of 21 GWDC of utility-scale PV plants in the United States, J. Renew. Sust. Energ. 12, 043501 (2020) [CrossRef] [Google Scholar]
  14. K. Kiefer, B. Farnung, B. Müller, Degradation in PV power plants: theory and practice, in 35th EU PVSEC, 2018 [Google Scholar]
  15. D.C. Jordan, S.R. Kurtz, K. VanSant, J. Newmiller, Compendium of photovoltaic degradation rates, Prog. Photovolt.: Res. Appl. 24, 978 (2016) [CrossRef] [Google Scholar]
  16. A. Phinikarides, N. Kindyni, G. Makrides, G.E. Georghiou, Review of photovoltaic degradation rate methodologies, Renew. Sust. Energ. Rev. 40, 143 (2014) [CrossRef] [Google Scholar]
  17. S. Lindig, I. Kaaya, K. Weiß, D. Moser, M. Topic, Review of statistical and analytical degradation models for photovoltaic modules and systems as well as related improvements, IEEE J. Photovolt. 8, 1773 (2018) [CrossRef] [Google Scholar]
  18. M. Theristis, A. Livera, C.B. Jones, G. Makrides, G.E. Georghiou, J.S. Stein, Nonlinear photovoltaic degradation rates: modeling and comparison against conventional methods, IEEE J. Photovolt. 10, 1112 (2020) [CrossRef] [Google Scholar]
  19. I. Romero-Fiances, A. Livera, M. Theristis, G. Makrides, J.S. Stein, G. Nofuentes, J. de la Casa, G.E. Georghiou, Impact of duration and missing data on the long-term photovoltaic degradation rate estimation, Renew. Energy 181, 738 (2022) [CrossRef] [Google Scholar]
  20. IEA PVPS Task 13, Assessment of performance loss rate of PV power systems, performance, operation and reliability of photovoltaic systems (2021) [Google Scholar]
  21. D.C. Jordan, C. Deline, S.R. Kurtz, G.M. Kimball, M. Anderson, Robust PV degradation methodology and application, robust PV degradation methodology and application, IEEE J. Photovolt. 8, 525 (2018) [CrossRef] [Google Scholar]
  22. IEA PVPS Task 13, Service life estimation for photovoltaic modules, performance, operation and reliability of photovoltaic system (2021) [Google Scholar]
  23. M.G. Deceglie, A. Nag, A. Shinn, G. Kimball, D. Ruth, D. Jordan, J. Yan, K. Anderson, K. Perry, M. Mikofski, M. Muller, W. Vining, C. Deline,RdTools, version 2.0.5, Computer Software [Google Scholar]
  24. G. Guerra, P. Mercade Ruiz, L. Landberg, A data-driven model for solar inverters, in 37th EU PVSEC, 2020 [Google Scholar]
  25. D. Optiz, R. Maclin, Popular ensemble methods: an empirical study, J. Artif. Intell. Res. 11, 169 (1999) [CrossRef] [Google Scholar]
  26. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, PyTorch: an imperative style, high-performance deep learning library, Adv. Neural Inf. Process. Syst. 32, 8024 (2019) [Google Scholar]
  27. H. van Hasselt, A. Guez, A.D. Silver, Deep reinforcement learning with double Q-learning, in AAAI'16: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (ACM, 2016), pp. 2094–2100 [Google Scholar]
  28. T.P. Lillicrap, J.J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, D. Wierstra, Continuous control with deep reinforcement learning, arXiv:1509.02971 (2019) [Google Scholar]
  29. M. Raissi, P. Perdikaris, G.E. Karniadakis, Physics informed deep learning (Part I): data-driven solutions of nonlinear partial differential equations, arXiv:1711.10561 (2017) [Google Scholar]
  30. https://github.com/NREL/rdtools/blob/master/docs/degradation_and_soiling_example_pvdaq_4.ipynb [Google Scholar]
  31. D. Rangel-Martinez, K.D.P. Nigam, L.A. Ricardez-Sandoval, Machine learning on sustainable energy: a review and outlook on renewable energy systems, catalysis, smart grid and energy storage, Chem. Eng. Res. Des. 174, 414 (2021) [CrossRef] [Google Scholar]

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