EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 15 (2024) EPJ Photovolt., 15 (2024) 12 References
Issue
EPJ Photovolt.
Volume 15, 2024
Special Issue on ‘EU PVSEC 2023: State of the Art and Developments in Photovoltaics’, edited by Robert Kenny and João Serra
Article Number 12
Number of page(s) 11
DOI https://doi.org/10.1051/epjpv/2024011
Published online 09 April 2024
  1. IEA, Electricity security in tomorrow's power systems, 2020. https://www.iea.org/articles/electricity-security-in-tomorrow-s-power-systems [Google Scholar]
  2. IEA, Renewables 2019: market analysis and forecast from 2019 to 2024, 2020. https://www.iea.org/reports/renewables-2019 [Google Scholar]
  3. IEA, Introduction to system integration of renewables: decarbonising while meeting growing demand, 2020. 2020. https://www.iea.org/reports/introduction-to-system-integration-of-renewables [Google Scholar]
  4. M. Mahoor, A. Majzoobi, A. Khodaei, Distribution asset management through coordinated microgrid scheduling, IET Smart Grid. 1, 159 (2018) [Google Scholar]
  5. H. Sadeghian, Z. Wang, A novel impact-assessment framework for distributed PV installations in low-voltage secondary networks, Renew. Energy 147, 2179 (2020) [Google Scholar]
  6. International Renewable Energy Agency, Innovation landscape for a renewable-powered future, 2019, https://www.irena.org/publications/2019/Feb/Innovation-landscape-for-a-renewable-powered-future [Google Scholar]
  7. H.T.C. Pedro, C.F.M. Coimbra, Assessment of forecasting techniques for solar power production with no exogenous inputs, Sol. Energy 86, 7 (2012). https://doi.org/10.1016/j.solener.2012.04.004 [Google Scholar]
  8. B. Wolff, O. Kramer, D. Heinemann, Selection of numerical weather forecast features for PV power predictions with random forests, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (2017), pp. 78–91. https://doi.org/10.1007/978-3-319-50947-1_8 [Google Scholar]
  9. T. Schmidt et al., Short-term solar forecasting based on sky images to enable higher PV generation in remote electricity networks, Renew. Energy Environ. Sustain. 2, 23 (2017) [CrossRef] [EDP Sciences] [Google Scholar]
  10. L. Visser, T. AlSkaif, W. van Sark, Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution, Renew. Energy 183, 267 (2022) [CrossRef] [Google Scholar]
  11. S. Theocharides, G. Makrides, Day-ahead solar photovoltaic forecasting for EAC supply portfolios (Nicosia, Cyprus, 2022) [Google Scholar]
  12. S. Pretto et al., A new probabilistic ensemble method for an enhanced day-ahead PV power forecast, IEEE J. Photovol. 12, 581 (2022) [CrossRef] [Google Scholar]
  13. D. Caputo et al., Photovoltaic plants predictive model by means of ANN trained by a hybrid evolutionary algorithm, in The 2010 International Joint Conference on Neural Networks (IJCNN) (IEEE, 2010), pp. 1–6. https://doi.org/10.1109/IJCNN.010.5596782 [Google Scholar]
  14. A. Mellit, A.M. Pavan, A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy, Sol. Energy 84, 807 (2010) [CrossRef] [Google Scholar]
  15. Y. Xue et al., Voltage stability and sensitivity analysis of grid-connected photovoltaic systems, in 2011 IEEE Power and Energy Society General Meeting, (IEEE, 2011), pp. 1–7 [Google Scholar]
  16. S. Pelland et al., Photovoltaic and solar forecasting: state of the art, in International Energy Agency: Photovoltaic Power Systems Programme, (Report IEA PVPS T14, 2013), pp. 1–40. https://www.researchgate.net/publication/259717376_Photovoltaic_and_Solar_Forecasting_State_of_the_Art, https://doi.org/978-3-906042-13-8 [Google Scholar]
  17. W. Glassley et al., California Renewable Energy Forecasting, Resource Data and Mapping (California Energy Commission, 2011), Publication number: CEC-500-2014-026 [Google Scholar]
  18. A. Yona et al., Application of neural network to 24-hour-ahead generating power forecasting for PV system, in 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, (IEEE, 2008) pp.1 –6. https://doi.org/10.1109/PES.2008.4596295 [Google Scholar]
  19. J.G. Da Silva Fonseca Junior et al., Forecasting regional photovoltaic power generation − a comparison of strategies to obtain one-day-ahead data, Energy Procedia 57, 1337 (2014) [Google Scholar]
  20. A. Dolara et al., A physical hybrid artificial neural network for short term forecasting of PV plant power output, Energies 8, 1138 (2015) [CrossRef] [Google Scholar]
  21. A. Gandelli et al., Hybrid model analysis and validation for PV energy production forecasting, in 2014 International Joint Conference on Neural Networks (IJCNN) , (IEEE, 2014), pp. 1957–1962. https://doi.org/10.1109/IJCNN.2014. 6889786 [Google Scholar]
  22. J. Shi et al., Forecasting power output of photovoltaic system based on weather classification and support vector machine,in 2011 IEEE Industry Applications Society Annual Meeting (IEEE, 2011) pp. 1–6. https://doi.org/10.1109/TIA.2012.2190816 [Google Scholar]
  23. M. Paulescu et al., Weather Modeling and Forecasting of PV Systems Operation in Green Energy and Technology (Springer, London, 2013). https://doi.org/10.1007/978-1-4471-4649-0 [Google Scholar]
  24. D. Yang et al., A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: towards carbon neutrality, Renew. Sustain. Energy Rev. 161, 112348 (2022) [Google Scholar]
  25. A. Mellit, A.M. Pavan, V. Lughi, Deep learning neural networks for short-term photovoltaic power forecasting, Renew. Energy 172, 276 (2021) [CrossRef] [Google Scholar]
  26. T. Chen, C. Guestrin, XGBoost: a scalable tree boosting system, in KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016 (ACM, 2016), pp. 785–794. https://doi.org/10.1145/2939672.2939785 [Google Scholar]
  27. A. Livera et al., Advanced diagnostic approach of failures for grid-connected photovoltaic (PV) systems, in 35th European Photovoltaic Solar Energy Conference (EU PVSEC), (2018), pp. 1548–1553. https://doi.org/10.4229/35thEUPVSEC20182018-6BO.6.5 [Google Scholar]
  28. W.C.Skamarock-NCAR/UCAR et al., ARW modelling system userguide − V.3, p. 408, 2016. https://doi.org/10.5065/D68S4MVH [Google Scholar]
  29. D. Orrell et al., Model error in weather forecasting, Nonlinear Process. Geophys. 8, 357 (2001). https://doi.org/10.5194/npg-8-357-2001 [CrossRef] [Google Scholar]
  30. P. Lynch, The origins of computer weather prediction and climate modeling, J. Comput. Phys. 227, 3431 (2008) [Google Scholar]
  31. P. Ineichen, R. Perez, A new airmass independent formulation for the Linke turbidity coefficient, Sol. Energy. 73, 151 (2002) [Google Scholar]
  32. R. Perez et al., A new operational model for satellite-derived irradiances: description and validation, Sol. Energy. 73, 307 (2002) [Google Scholar]
  33. 6. IEC, Photovoltaic system performance − Part 1: monitoring, IEC 61724-1, 2017 [Google Scholar]
  34. G. Makrides et al., Potential of photovoltaic systems in countries with high solar irradiation, Renew. Sustain. Energy Rev. 14, 754 (2010) [Google Scholar]
  35. C. Tofallis, A better measure of relative prediction accuracy for model selection and model estimation, J. Oper. Res. Soc. 66, 8 (2015) [Google Scholar]
  36. R.J. Hyndman, A.B. Koehler, Another look at measures of forecast accuracy, Int. J. Forecast. 22, 679 (2006) [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.