EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 15 (2024) EPJ Photovolt., 15 (2024) 17 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 17
Number of page(s) 15
DOI https://doi.org/10.1051/epjpv/2024013
Published online 08 May 2024
  1. IEA, Solar pv power generation in the net zero scenario, 2015-2030. Available from: https://www.iea.org/energy-system/renewables/solar-pv#tracking (accessed: 2024-03-26) [Google Scholar]
  2. A. Salah Saidi, Impact of grid-tied photovoltaic systems on voltage stability of Tunisian distribution networks using dynamic reactive power control, Ain Shams Eng. J. 13, 101537 (2022). https://doi.org/10.1016/j.asej.2021.06.023 [CrossRef] [Google Scholar]
  3. H. Ye, B. Yang, Y. Han, N. Chen, State-of-the-art solar energy forecasting approaches: critical potentials and challenges, Front. Energy Res. 10, 875790 (2022). https://doi.org/10.3389/fenrg.2022.875790 [CrossRef] [Google Scholar]
  4. J. Zeng, W. Qiao, Short-term solar power prediction using an RBF neural network, in 2011 IEEE Power and Energy Society General Meeting (2011), pp. 1–8. https://doi.org/10.1109/PES.2011.6039204 [Google Scholar]
  5. A. Dairi, F. Harrou, Y. Sun, S. Khadraoui, Short-term forecasting of photovoltaic solar power production using variational auto-encoder driven deep learning approach, Appl. Sci. 10, 4 (2020). https://doi.org/10.3390/app10238400 [CrossRef] [Google Scholar]
  6. A. Fentis, L. Bahatti, M. Mestari, B. Chouri, Short-term solar power forecasting using Support Vector Regression and feed-forward NN, in 2017 15th IEEE International New Circuits and Systems Conference (NEWCAS) (2017), pp. 405–408. https://doi.org/10.1109/NEWCAS.2017.8010191 [Google Scholar]
  7. A. Starosta, K. Kaushik, P. Jhaveri, N. Munzke, M. Hiller, A Comparative Analysis of Forecasting Methods for Photovoltaic Power and Energy Generation with and without Exogenous Inputs (WIP-Renewable Energies (WIP), 2021), pp. 938–945. https://doi.org/10.4229/EUPVSEC20212021-5BO.7.1 [Google Scholar]
  8. F. Almonacid, P. Pérez-Higueras, E.F. Fernández, L. Hontoria, A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator, Energy Convers. Manag. 85, 389 (2014). https://doi.org/10.1016/j.enconman.2014.05.090 [CrossRef] [Google Scholar]
  9. P. Bacher, H. Madsen, H. Nielsen, Online short-term solar power forecasting, Sol. Energy 83, 1772 (2009). https://doi.org/10.1016/j.solener.2009.05.016 [Google Scholar]
  10. J. Lehmann, C. Koessler, Benchmark of eight commercial solutions for deterministic intra-day solar forecast, EPJ Photovolt. 14, 15 (2023). https://doi.org/10.1051/epjpv/2023006 [Google Scholar]
  11. M.R. Maghami, H. Hizam, C. Gomes, M.A. Radzi, M.I. Rezadad, S. Hajighorbani, Power loss due to soiling on solar panel: a review, Renew. Sustain. Energy Rev. 59, 1307 (2016). https://doi.org/10.1016/j.rser.2016.01.044 [CrossRef] [Google Scholar]
  12. G.G. Kim, W. Lee, B.G. Bhang, J.H. Choi, H.K. Ahn, Fault detection for photovoltaic systems using multivariate analysis with electrical and environmental variables, IEEE J. Photovolt. 11, 202 (2021). https://doi.org/10.1109/JPHOTOV.2020.3032974 [CrossRef] [Google Scholar]
  13. A. Alcañiz, D. Grzebyk, H. Ziar, O. Isabella, Trends and gaps in photovoltaic power forecasting with machine learning, Energy Rep. 9, 447 (2023). https://doi.org/10.1016/j.egyr.2022.11.208 [CrossRef] [Google Scholar]
  14. Y. Chaibi, M. Malvoni, A. Chouder, M. Boussetta, M. Salhi, Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems, Energy Convers. Manag. 196, 330 (2019). https://doi.org/10.1016/j.enconman.2019.05.086 [CrossRef] [Google Scholar]
  15. A. Dolara, G.C. Lazaroiu, S. Leva, G. Manzolini, Experimental investigation of partial shading scenarios on PV (photovoltaic) modules, Energy 55, 466 (2013). https://doi.org/10.1016/j.energy.2013.04.009 [CrossRef] [Google Scholar]
  16. R. Ahmad, A.F. Murtaza, H. Ahmed Sher, U. Tabrez Shami, S. Olalekan, An analytical approach to study partial shading effects on PV array supported by literature, Renew. Sustain. Energy Rev. 74, 721 (2017). https://doi.org/10.1016/j.rser.2017.02.078 [CrossRef] [Google Scholar]
  17. A. Babatunde, S. Abbasoglu, M. Senol, Analysis of the impact of dust, tilt angle and orientation on performance of PV plants, Renew. Sustain. Energy Rev. 90, 1017 (2018). https://doi.org/10.1016/j.rser.2018.03.102 [CrossRef] [Google Scholar]
  18. S. Pareek, R. Dahiya, Enhanced power generation of partial shaded photovoltaic fields by forecasting the interconnection of modules, Energy 95, 561 (2016). https://doi.org/10.1016/j.energy.2015.12.036 [CrossRef] [Google Scholar]
  19. M.J. Mayer, G. Gróf, Extensive comparison of physical models for photovoltaic power forecasting, Energy 283, 116239 (2021). https://doi.org/10.1016/j.apenergy.2020.116239 [Google Scholar]
  20. D. Masa-Bote, M. Castillo-Cagigal, E. Matallanas, E. Caamaño-Martín, A. Gutiérrez, F. Monasterio-Huelín, J. Jiménez-Leube, Improving photovoltaics grid integration through short time forecasting and self-consumption, Appl. Energy 125, 103 (2014). https://doi.org/10.1016/j.apenergy.2014.03.045 [CrossRef] [Google Scholar]
  21. C.C. Aggarwal, Neural Networks and Deep Learning (Springer Cham, 2018). https://doi.org/10.1007/978-3-319-94463-0 [CrossRef] [Google Scholar]
  22. M. Rana, A. Rahman, Multiple steps ahead solar photovoltaic power forecasting based on univariate machine learning models and data re-sampling, Sustain. Energy Grids Netw. 21, 100286 (2020). https://doi.org/10.1016/j.segan.2019.100286 [CrossRef] [Google Scholar]
  23. H. Malki, N. Karayiannis, M. Balasubramanian, Shortterm electric power load forecasting using feedforward neural networks, (2024) Vol. 21, pp. 157–167 [Google Scholar]
  24. A. Yona, T. Senjyu, T. Funabashi, Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system, in 2007 IEEE Power Engineering Society General Meeting (2007), pp. 1–6. https://doi.org/10.1109/PES. 2007.386072 [Google Scholar]
  25. S. Srivastava, S. Lessmann, A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data, Sol. Energy 162, 232 (2018). https://doi.org/10.1016/j.solener.2018.01.005 [CrossRef] [Google Scholar]
  26. M. Gao, J. Li, F. Hong, D. Long, Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM, Energy 187, 115838 (2019). https://doi.org/10.1016/j.energy.2019.07.168 [CrossRef] [Google Scholar]
  27. F. Harrou, F. Kadri, Y. Sun, Forecasting of photovoltaic solar power production using LSTM approach in Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems (IntechOpen, 2020). https://doi.org/10.5772/intechopen.91248 [Google Scholar]
  28. C.H. Liu, J.C. Gu, M.T. Yang, A simplified LSTM neural networks for one day-ahead solar power forecasting, IEEE Access 9, 17174 (2021). https://doi.org/10.1109/ACCESS.2021.3053638 [CrossRef] [Google Scholar]
  29. M. Awad, R. Khanna, Support Vector Regression (Apress, Berkeley, CA, 2015), pp. 67–80. https://doi.org/10.1007/978-1-4302-5990-9_4 [Google Scholar]
  30. M. Awad, R. Khanna, Support Vector Regression (Apress, Berkeley, CA, 2015), pp. 70–71. https://doi.org/10.1007/978-1-4302-5990-9_4 [Google Scholar]
  31. A. Anghel, N. Papandreou, T. Parnell, A. Palma, H. Pozidis, arXiv:1809.04559 (2018) [Google Scholar]
  32. V.K. Ayyadevara, Gradient Boosting Machine (Apress, Berkeley, CA, 2018), pp. 117–134. https://doi.org/10.1007/978-1-4842-3564-5_6 [Google Scholar]
  33. H. Li, Machine Learning Methods (Springer Singapore, 2023). https://doi.org/10.1007/978-981-99-3917-6 [Google Scholar]
  34. D.W. (DWD), CDC - Climate Data Center, https://cdc.dwd.de/portal/ (Accessed: 2024-01-01) [Google Scholar]
  35. D.W. (DWD), Index of weather, https://opendata.dwd.de/weather/ (Accessed: 2024-01-01) [Google Scholar]
  36. W. Holmgren, C. Hansen, M. Mikofski, pvlib python: a python package for modeling solar energy systems, J. Open Source Softw. 3, 884 (2018). https://doi.org/10.21105/joss.00884 [CrossRef] [Google Scholar]
  37. H. Chen, X. Chang, Photovoltaic power prediction of LSTM model based on Pearson feature selection, in 2021 International Conference on Energy Engineering and Power Systems (2021), Vol. 7, pp. 1047–1054. https://doi.org/10.1016/j.egyr.2021.09.167 [Google Scholar]
  38. M.F.N. Tanvir Ahmad, S. Sobhan, Comparative Analysis between Single Diode and Double Diode Model of PV Cell: Concentrate Different Parameters Effect on Its Efficiency, J. Power Energy Eng. 4, 31 (2016). https://doi.org/10.4236/jpee.2016.43004 [Google Scholar]
  39. M.H. Qais, H.M. Hasanien, S. Alghuwainem, K. Loo, M. Elgendy, R.A. Turky, Accurate Three-Diode model estimation of Photovoltaic modules using a novel circle search algorithm, Ain Shams Eng. J. 13, 101824 (2022). https://doi.org/10.1016/j.asej.2022.101824 [CrossRef] [Google Scholar]
  40. E. Batzelis, G. Anagnostou, C. Chakraborty, B. Pal, Computation of the Lambert W function in photovoltaic modeling in ELECTRIMACS 2019. Lecture Notes in Electrical Engineering, Vol. 615 (2020). https://doi.org/10.1007/978-3-030-37161-6_44 [Google Scholar]
  41. S. Ghosh, J. Roy, C. Chakraborty, A model to determine soiling, shading and thermal losses from PV yield data, Clean Energy 6, 372 (2022). https://doi.org/10.1093/ce/zkac014 [Google Scholar]
  42. T. Selmi, M. Abdul-Niby, L. Devis, A. Davis, P&O MPPT implementation using MATLAB/Simulink, in 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER) (2014), pp. 1–4. https://doi.org/10.1109/EVER.2014.6844065 [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.