Application of similarity analysis in PV sources generation forecasting for energy clusters

Radomir Rogus; Maciej Sołtysik; Rafał Czapaj

doi:10.1051/e3sconf/20198401009

All issues

Volume 84 (2019)

E3S Web Conf., 84 (2019) 01009

References

Open Access

Issue		E3S Web Conf. Volume 84, 2019 14^th International Scientific Conference “Forecasting in Electric Power Engineering” (PE 2018)


Article Number		01009
Number of page(s)		8
Section		Forecasting in Electric Power Engineering
DOI		https://doi.org/10.1051/e3sconf/20198401009
Published online		11 February 2019

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[1] C. Monteiro, T. Santos et. al., Energies 6, 2624–2643 (2013) [CrossRef] [Google Scholar]

[2] Consortium: KAPE, WiseEuropa, Atmoterm, KIER, Koncepcja Funkcjonowania Klastrów Energii w Polsce (eng. The concept of functioning of Eneregy Clusters in Poland), (2017) [Google Scholar]

[3] C. Wan, J. Zhao, Y. Song, Z. Xu, J. Lin, Z. Hu, CSEE JPES 4, 38–46 (2015) [CrossRef] [Google Scholar]

[4] U. K. Das, K.S. Tey et al., Renew. Sust. Energ. Rev. 81, 912–928 (2018) [CrossRef] [Google Scholar]

[5] Publicly available meteorological measurement and observation data, Instytut Meteorologii i Gospodarki Wodnej, https://danepubliczne.imgw.pl/ [Google Scholar]

[6] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning, Second Edition (Springer, 2008) [Google Scholar]

[7] A. Mandelbaum, D. Weinshall, Computing Research Repository, abs/1709.09844 (2017) [Google Scholar]

[8] J. Koronacki, J. Ćwik, Statystyczne systemy uczące się, wydanie drugie, (eng. Statistical learning systems, second edition), (Exit, Warsaw, 2008) [Google Scholar]

[9] W. Rea, M. Reale, C. Cappelli, J. A. Brown, Economic Reviews 29, 754–777 (2010) [CrossRef] [Google Scholar]

[10] E. Gatnar, Symboliczne metody klasyfikacji danych (eng. Symbolic methods of data classification), (PWN, 1998) [Google Scholar]

[11] N. Patel, S. Upadhyay, IJCA 12, 20–25 (2012) [CrossRef] [Google Scholar]