Open Access
Issue
E3S Web Conf.
Volume 14, 2017
Energy and Fuels 2016
Article Number 01019
Number of page(s) 10
Section Energy
DOI https://doi.org/10.1051/e3sconf/20171401019
Published online 15 March 2017
  1. A. G. R. Vaz, B. Elsinga, W. G. J. H. M. van Sark, M. C. Brito, An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands Renewable Energy, 85, 631–641 (2016) [CrossRef]
  2. S. Kim, Y. Seo, V. P. Singh, Estimating Global Solar Irradiance for Optimal Photovoltaic System Procedia Engineering, 154, 1237–1242 (2016) [CrossRef]
  3. S. S. Soman, H. Zareipour, O. Malik, P. Mandal, A review of wind power and wind speed forecasting methods with different time horizons In North American Power Symposium (NAPS), 2010 (pp. 1–8). IEEE. (2010)
  4. J. Jurasz, A. Piasecki, Evaluation of the Complementarity of Wind Energy Resources, Solar Radiation and Flowing Water–a Case Study of Piła Acta Energetica 2 (2016). [CrossRef]
  5. C. E. Hoicka, I. H. Rowlands Solar and wind resource complementarity: Advancing options for renewable electricity integration in Ontario, Canada Renewable Energy, 36(1), 97–107 (2011) [CrossRef]
  6. X. Luo, J. Wang, M. Dooner, J. Clarke, Overview of current development in electrical energy storage technologies and the application potential in power system operation Applied Energy, 137, 511–536, (2015) [CrossRef]
  7. M. A. Ramli, A. Hiendro, Y. A. Al-Turki, Techno-economic energy analysis of wind/solar hybrid system: Case study for western coastal area of Saudi Arabia Renewable Energy, 91, 374–385, (2016) [CrossRef]
  8. S. Weitemeyer, D. Kleinhans, T. Vogt, C. Agert, Integration of Renewable Energy Sources in future power systems: The role of storage Renewable Energy, 75, pp. 14–20 (2015) [CrossRef]
  9. R. Kasperek, M. Wiatkowski: Hydropower generation on the Nysa Kłodzka river Ecological Chemistry and Engineering, 21(2), pp. 327–336 (2014)
  10. Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC.
  11. I. Graabak, M. Korpås, Balancing of variable wind and solar production in Continental Europe with Nordic hydropower–A review of simulation studies Energy Procedia, 87, 91–99 (2016) [CrossRef]
  12. T. Niedzielski, B. Miziński, M. Kryza, P. Netzel, M. Wieczorek, M. Kasprzak, P. Migoń, M. Szymanowski, J. Jeziorska, M. Witek, HydroProg: a system for hydraulic forecasting in real time, based on the multimodelling approach, Meteorology Hydrology and Water Management vol. 2 issue 2. IMGW-PIB, Warszawa, pp. 65–72. (2014)
  13. U. Smyczyńska, J. Smyczyńska, & R. Tadeusiewicz. Neural modelling of growth hormone therapy for the prediction of therapy results Bio-Algorithms and Med-Systems. 11(1): 33–45. doi: 10.1515/bams-2014-0021(2015)
  14. J. Jurasz, J. Mikulik, Day ahead electric power load forecasting by WT-ANN, Przegląd Elektrotechniczny, 4, pp. 152–154, (2016)
  15. A. Piasecki, J. Jurasz, W. Marszelewski. Wykorzystanie wielowarstwowych sztucznych sieci neuronowych do średnioterminowego prognozowania poboru wody-studium przypadku. Ochrona Srodowiska 38.2 (2016): 17.
  16. J. Adamowski, H. F. Chan A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28–40. doi:10.1016/j.jhydrol.2011.06.013 (2011) [CrossRef]
  17. D. J. MacKay, Information theory, inference and learning algorithms. Cambridge university press (2003)
  18. G. J. Bowden, G. C. Dandy, H. R. Maier: Input determination for neural network models in water resources applications. Part 1 – background and methodology. Journal of Hydrology, Vol. 301, No. 1–4, pp. 75–92 (2005) [CrossRef]
  19. A. Piasecki, J. Jurasz, and Skowron, R. Application of artificial neural networks (ANN) in Lake Drwęckie water level modeling. Limnological Review, 15(1), 21–30. (2015)
  20. T. Niedzielski, B. Miziński, D. Yu, Hydrological forecasting in real time: an experimental integrated approach, et. al.: J. Jasiewicz, Z. Zwoliński, H. Mitasova, T. Hengl (Eds.), Geomorphometry for Geosciences, Bogucki Wydawnictwo Naukowe, Adam Mickiewicz University in Poznań – Institute of Geoecology and Geoinformation, Poznań, pp. 97–101 (2015)
  21. T. Niedzielski, B. Miziński Real-time hydrograph modelling in the upper Nysa Kłodzka river basin (SW Poland): a two-model hydrologic ensemble prediction approach. Stochastic Environmental Research and Risk Assessment, Online first: 28 April 2016 DOI: 10.1007/s00477-016-1251-5 (2016)
  22. O. Bozorg-Haddad, M. Zarezadeh-Mehrizi, M. Abdi-Dehkordi, H. A. Loáiciga, M. A. Mariño, A self-tuning ANN model for simulation and forecasting of surface flows, Water Resources Management, 30(9), pp 2907–2929. DOI:10.1007/s11269-016-1301-2 (2016) [CrossRef]
  23. B. Kang, Y.H. Ku, Y.D Kim, A case study for ANN-based rainfall–runoff model considering antecedent soil moisture conditions in Imha Dam watershed, Korea. Environmental Earth Sciences, 74(2), pp 1261–1272, DOI:10.1007/s12665-015-4117-0 (2015) [CrossRef]
  24. K. Shibata, Y. Ikeda, Effect of number of hidden neurons on learning in large-scale layered neural networks. Proc. of the ICROS-SICE International Joint Conference, pp. 5008–5013, (2009
  25. J. Nash, and J. V. Sutcliffe, River flow forecasting through conceptual models part I—A discussion of principles, Journal of hydrology 10.3 (1970): 282–290 [CrossRef]
  26. F. Mosteller, A k-sample slippage test for an extreme population. In: Selected Papers of Frederick Mosteller, Springer, New York pp. 101–109, (2006) [CrossRef]

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.