Issue |
EPJ Photovolt.
Volume 15, 2024
Special Issue on ‘EU PVSEC 2024: State of the Art and Developments in Photovoltaics’, edited by Robert Kenny and Gabriele Eder
|
|
---|---|---|
Article Number | 33 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/epjpv/2024028 | |
Published online | 21 October 2024 |
- DNV, Energy transition outlook. 2023. https://www.dnv.com/energy-transition-outlook/download [Google Scholar]
- M. Morey, N. Gupta, M.M. Garg, A. Kumar, A comprehensive review of grid-connected solar photovoltaic system: architecture, control, and ancillary services, Renew. Energy Focus 45, 307 (2023) [CrossRef] [Google Scholar]
- Fraunhofer-ISE, Photovoltaics report. 2023. https://www.ise.fraunhofer.de/content/dam/ise/de/documents/publications/studies/Photovoltaics-Report.pdf. Accessed: 2024-04-30 [Google Scholar]
- R. Grab, F. Hans, M.I.R. Flores, H. Schmidt, S. Rogalla, B. Engel, Modeling of photovoltaic inverter losses for reactive power provision, IEEE Access 10, 108506 (2022) [CrossRef] [Google Scholar]
- K. Passow, L. Ngan, A. Panchula, Self-reported field efficiency of utility-scale inverters, in 2014 IEEE 40th Photovoltaic Specialist Conference (PVSC) (2014), pp. 1963–1968 [Google Scholar]
- L. Kaci, D.A.H. Arab, R. Zirmi, S. Semaoui, S. Boulahchiche, Solar inverter performance prediction, in 2020 6th International Symposium on New and Renewable Energy (SIENR) (2021), pp. 1–5 [Google Scholar]
- N. Allet, F. Baumgartner, J. Sutterlueti, S. Sellner, M. Pezzotti, Inverter performance under field conditions, in 27th EU PVSEC (2012) [Google Scholar]
- S. Krauter, J. Bendfeld, Microinverter pv systems: new efficiency rankings and formula for energy yield assessment for any pv panel size at different microinverter types, in 8th World Conference on Photovoltaic Energy Conversion (2022) [Google Scholar]
- D.L. King, S. Gonzalez, G.M. Galbraith, W.E. Boyson, Performance model for grid-connected photovoltaic inverters, tech. rep., Sandia National Laboratories, 2007. Accessed: 2024-04–30 [Google Scholar]
- S. Suarez, V. Daniel, G.A. Navas, J. Vilela, I. Fernandez, S. Rodríguez-Conde, Central inverter testing under real outdoor conditions. a controllable analysis under non-controllable conditions using statistics. a real case study, in 40th EU PVSEC (2023) [Google Scholar]
- C. Hansen, J. Johnson, R. Darbali-Zamora, N.S. Gurule, S. Gonzalez, M. Theristis, Modeling inverters with multiple inputs: Test procedure for measuring efficiency, in 8th World Conference on Photovoltaic Energy Conversion (2022) [Google Scholar]
- E. Engel, N. Engel, A review on machine learning applications for solar plants, Sensors 22, 23 (2022) [Google Scholar]
- I.E. Commission, Photovoltaic systems − power conditioners − procedure for measuring efficiency, standard, International Electrotechnical Commission (1999) [Google Scholar]
- I.E. Commission, Maximum power point tracking efficiency of grid connected photovoltaic inverters, standard, International Electrotechnical Commission (2020) [Google Scholar]
- PVSYST, European or cec efficiency. 2023. https://www.pvsyst.com/help/inverter_euroeff.htm. Accessed: 2024-04-30 [Google Scholar]
- Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms, 1st ed. (Chapman and Hall/CRC, 2012) [CrossRef] [Google Scholar]
- E. Goan, C. Fookes, Bayesian Neural Networks: An Introduction and Survey (Springer International Publishing, 2020), pp. 45–87 [Google Scholar]
- D.M. Titterington, Bayesian methods for neural networks and related models, Stat. Sci. 19, 128 (2004) [CrossRef] [PubMed] [Google Scholar]
- J. Lampinen, A. Vehtari, Bayesian approach for neural networks—review and case studies, Neural Netw. 14, 257 (2001) [CrossRef] [Google Scholar]
- V. Nemani, L. Biggio, X. Huan, Z. Hu, O. Fink, A. Tran, Y. Wang, X. Zhang, C. Hu, Uncertainty quantification in machine learning for engineering design and health prognostics: a tutorial, Mech. Syst. Signal Process. 205, 110796 (2023) [CrossRef] [Google Scholar]
- B. Harnist, S. Pulkkinen, T. Mäkinen, Deuce v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties, Geosci. Model Dev. 17, 3839 (2024) [Google Scholar]
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E.Z. 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, in NeurIPS, edited by H.M. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché Buc, E.B. Fox, R. Garnett (2019), pp. 8024–8035 [Google Scholar]
- S. Lee, H. Kim, J. Lee, Graddiv: adversarial robustness of randomized neural networks via gradient diversity regularization, IEEE Trans. Pattern Anal. Mach. Intell. 45, 2645 (2022) [Google Scholar]
- 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 (2015). https://doi.org/10.48550/arXiv.1509.02971 [Google Scholar]
- H. van Hasselt, A. Guez, D. Silver, Deep reinforcement learning with double q-learning, arXiv:1509.06461 (2015). https://doi.org/10.48550/arXiv.1509.06461 [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) [CrossRef] [EDP Sciences] [Google Scholar]
- S. Kullback, R.A. Leibler, On information and sufficiency, Ann. Math. Stat. 22, 79 (1951) [CrossRef] [Google Scholar]
- G. Cybenko, Approximation by superpositions of a sigmoidal function, Math. Control Signals Syst. 2, 303 (1989) [CrossRef] [Google Scholar]
- A.D. Kiureghian, O. Ditlevsen, Aleatory or epistemic? does it matter? Struct. Safety 31, 105 (2009) [CrossRef] [Google Scholar]
- T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: a next-generation hyperparameter optimization framework, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019) [Google Scholar]
- J. Bergstra, R. Bardenet, Y. Bengio, B. Kégl, Algorithms for hyper-parameter optimization, in Advances in Neural Information Processing Systems, edited by J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, K. Weinberger (Curran Associates, Inc., 2011), Vol. 24 [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.