| Issue |
EPJ Photovolt.
Volume 17, 2026
Special Issue on ‘EU PVSEC 2025: State of the Art and Developments in Photovoltaics', edited by Robert Kenny and Carlos del Cañizo
|
|
|---|---|---|
| Article Number | 8 | |
| Number of page(s) | 17 | |
| DOI | https://doi.org/10.1051/epjpv/2026001 | |
| Published online | 13 February 2026 | |
- S. Lindig, J. Ascencio-Vásquez, J. Leloux, D. Moser, A. Reinders, Performance analysis and degradation of a large fleet of PV systems, IEEE J. Photovolt. 11, 1312 (2021). https://doi.org/10.1109/JPHOTOV.2021.3093049 [CrossRef] [Google Scholar]
- D.C. Jordan, K. Anderson, K. Perry, M. Muller, M. Deceglie, R. White, C. Deline, Photovoltaic fleet degradation insights, Prog. Photovolt. Res. Appl. 30, 1166 (2022). https://doi.org/10.1002/pip.3566 [CrossRef] [Google Scholar]
- A. Louwen, S. Lindig, G. Chowdhury, D. Moser, Climate- and technology-dependent performance loss rates in a large commercial photovoltaic monitoring dataset, Sol. RRL 8, 2300653 (2024). https://doi.org/10.1002/solr.202300653 [Google Scholar]
- S. Lindig, M. Herz, J. Ascencio-Vásquez, M. Theristis, B. Herteleer, J. Deckx, K. Anderson, Review of technical photovoltaic key performance indicators and the importance of data quality routines, Sol. RRL 8, 2400634 (2024). https://doi.org/10.1002/solr.202400634 [Google Scholar]
- IEA, Guidelines for operation and maintenance of photovoltaic power plants in different climates (IEA-PVPS, Paris, 2022) [Google Scholar]
- D.C. Jordan, S.R. Kurtz, Photovoltaic degradation rates - an analytical review, Prog. Photovolt. Res. Appl. 21, 12 (2013). https://doi.org/10.1002/pip.1182 [Google Scholar]
- P. Gupta, R. Singh, PV power forecasting based on data-driven models: a review, Int. J. Sustain. Eng. 14, 1733 (2021). https://doi.org/10.1080/19397038.2021.1986590 [Google Scholar]
- M.J. Mayer, G. Gróf, Extensive comparison of physical models for photovoltaic power forecasting, Appl. Energy 283, 116239 (2021). https://doi.org/10.1016/j.apenergy.2020.116239 [Google Scholar]
- IEA, Assessment of performance loss rate of PV power systems (IEA-PVPS, Paris, 2021) [Google Scholar]
- IEA, The use of advanced algorithms in PV failure monitoring (IEA-PVPS, Paris, 2021) [Google Scholar]
- K. Anderson, C. Hansen, W. Holmgren, A. Jensen, M. Mikofski, A. Driesse, Pvlib Python: 2023 project update, J. Open Source Softw. 8, 5994 (2023). https://doi.org/10.21105/joss.05994 [CrossRef] [Google Scholar]
- M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), E. Simoudis, J. Han, U.M. Fayyad (Eds.), (AAAI Press, Portland, 1996), pp. 226–231 [Google Scholar]
- C. Lartey, J. Liu, R. K. Asamoah, C. Greet, M. Zanin, W. Skinner, Effective outlier detection for ensuring data quality in flotation data modelling using machine learning (ML) algorithms, Minerals 14, 925 (2024). https://doi.org/10.3390/min14090925 [Google Scholar]
- G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T. Y. Liu, LightGBM: a highly efficient gradient boosting decision tree, in Advances in Neural Information Processing Systems 30 (NeurIPS 2017), I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (Eds.),(Curran Associates, Red Hook, NY, 2017), p. 3146. https://proceedings.neurips.cc/paper_files/paper/2017/file/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf [Google Scholar]
- G. Guerra, P. Mercade-Ruiz, G. Anamiati, L. Landberg, Long-term PV system modelling and degradation using neural networks, EPJ Photovolt. 14, 30 (2023). https://doi.org/10.1051/epjpv/2023018 [CrossRef] [EDP Sciences] [Google Scholar]
- M.G. Deceglie, K. Anderson, A. Shinn, N. Ambarish, M. Mikofski, M. Springer, J. Yan, K. Perry, S. Villamar, W. Vining, G.M. Kimball, D. Ruth, N. Moyer, Q. Nguyen, D. Jordan, M. Muller, C. Deline, RdTools [Computer software], Zenodo (2018). https://doi.org/10.5281/zenodo.1210316 [Google Scholar]
- D. Jordan, C. Deline, S. Kurtz, G. Kimball, M. Anderson, Robust PV degradation methodology and application, IEEE J. Photovolt. 8, 525 (2018). https://doi.org/10.1109/JPHOTOV.2017.2779779 [CrossRef] [Google Scholar]
- R.B. Cleveland, W.S. Cleveland, J.E. McRae, I. Terpenning, STL: a seasonal-trend decomposition procedure based on loess, J. Off. Stat. 6, 3 (1990). https://www.math.unm.edu/lil/Stat581/STL.pdf [Google Scholar]
- R. Killick, P. Fearnhead, I.A. Eckley, Optimal detection of changepoints with a linear computational cost, J. Am. Stat. Assoc. 107, 1590 (2012). https://doi.org/10.1080/01621459.2012.737745 [CrossRef] [Google Scholar]
- C. Truong, L. Oudre, N. Vayatis, Selective review of offline change point detection methods, Signal Process. 167, 107299 (2020). https://doi.org/10.1016/j.sigpro.2019.107299 [CrossRef] [Google Scholar]
- Z. Gao, X. Xiao, Y.-P. Fang, J. Rao, H. Mo, A selective review on information criteria in multiple change point detection, Entropy 26, 50 (2024). https://doi.org/10.3390/e26010050 [Google Scholar]
- M. Lavielle, Using penalized contrasts for the change-point problem, Signal Process. 85, 1501 (2005). https://doi.org/10.1016/j.sigpro.2005.01.012 [Google Scholar]
- K. Haynes, I.A. Eckley, P. Fearnhead, Computationally efficient changepoint detection for a range of penalties, J. Comput. Graph. Stat. 26, 134 (2017). https://doi.org/10.1080/10618600.2015.1116445 [CrossRef] [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.
