Advanced method for short‐term wind power prediction with multiple observation points using extreme learning machines
Author:
Affiliation:
1. School of Electrical and Information Engineering, The University of SydneyNSW 2006SydneyAustralia
2. School of Electrical Engineering and Telecommunications, The University of NSWSydneyAustralia
Publisher
Institution of Engineering and Technology (IET)
Subject
General Engineering,Energy Engineering and Power Technology,Software
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/joe.2017.0338
Reference81 articles.
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2. Concepts for the improved integration of wind power into the German interconnected system;Siemes P.;IET Renew. Power Gener.,2008
3. Analysis of demand response and wind integration in Germany's electricity market;Klobasa M.;IET Renew. Power Gener.,2010
4. Selected papers from the European wind energy association 2014, Barcelona;Muskulus M.;IET Renew. Power Gener.,2015
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