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Using artificial neural networks to determine the location of wind farms. Miedzna district case study

. PW. ISBN 978-83-7207-838-4 pp. 192. D odge Y. (ed.) 2003. A dictionary of statistics. Oxford. Oxford University Press. ISBN 0-19-850994-4 pp. 506. Energetyka Cieplna i Zawodowa 2009–2010. Vol. 12/2009, 1/2010. ISSN 1734-7823. GWEC 2014 Global wind statistics [online]. [Access 05.05.2016]. Available at: http://www.gwec.net/wp-content/uploads/2015/02/GWEC_GlobalWindStats2014_FINAL_10.2.2015.pdf J ing L., J i - hang C., J ing - yuan S., F ei H. 2012. Brief introduction of Back Propagation (BP) neural network algorithm and its improvement

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Perspectives on offshore wind farms development in chosen countries of European Union

-energie.de/sites/default/files/download/publication/z-fakten-zur-windenergie/bwe_abisz_3-2015_72dpi_final.pdf Danish Energy Agency 2012. Executive Order no. 68 of 26th January 2012. Danish Energy Agency 2018. Master data for wind turbines as at the end of December 2017 [online]. [Access 28.09.2017]. Available at: https://ens.dk/en/ourservices/statistics-data-key-figures-and-energy-maps/overview-energy-sector D awid L. 2017a. German support systems for onshore wind farms in the context of Polish acts limiting wind energy development. Journal of Water and Land Development. No. 34 p. 109–115. D awid L. 2017b. Chosen problems

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