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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: 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 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: 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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