FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W1994064251> ?p ?o ?g. }

• W1994064251 endingPage "60" @default.
• W1994064251 startingPage "49" @default.
• W1994064251 abstract "The real operation of a wind farm implies the solution of many different problems related to wind speed at a wind farm location site. Wind speed prediction and wind series reconstruction are the two examples of important problems tackled in wind farm management and prospection. Usually, wind speed prediction and reconstruction of wind series are carried out in wind farms using data from in situ measuring towers, usually named as Measure-Correlate-Predict methods (MCP). MCP processes consist, therefore, in the wind speed prediction or reconstruction from neighbor stations, using different methods. In this paper, we tackle the special case of real MCP operations in wind farms, in which the algorithms to reconstruct or predict the wind series must be extremely fast in order to be useful. We present the application of two state-of-the-art neural networks which have shown a very fast training time, with an excellent performance in terms of accuracy. Specifically, we show the application of Group Method of Data Handling and Extreme Learning Machines in the MCP reconstruction and prediction of wind speed series, in a real wind farm in Spain. A comparison in terms of computation time and accuracy with alternative algorithms in the literature is also carried out. Finally, we show a real implementation of the Group Method of Data Handling (GMDH) and Extreme Learning Machine (ELM) in a software in use for real MCP operations in wind farms." @default.
• W1994064251 created "2016-06-24" @default.
• W1994064251 creator A5024548236 @default.
• W1994064251 creator A5025107878 @default.
• W1994064251 creator A5060780612 @default.
• W1994064251 creator A5065414106 @default.
• W1994064251 creator A5072580033 @default.
• W1994064251 creator A5035720793 @default.
• W1994064251 date "2013-05-01" @default.
• W1994064251 modified "2023-10-18" @default.
• W1994064251 title "Very fast training neural-computation techniques for real measure-correlate-predict wind operations in wind farms" @default.
• W1994064251 cites W1968020751 @default.
• W1994064251 cites W1985504623 @default.
• W1994064251 cites W1993717606 @default.
• W1994064251 cites W2002762317 @default.
• W1994064251 cites W2003201814 @default.
• W1994064251 cites W2004799078 @default.
• W1994064251 cites W2012272234 @default.
• W1994064251 cites W2015665178 @default.
• W1994064251 cites W2021935701 @default.
• W1994064251 cites W2025037575 @default.
• W1994064251 cites W2026131661 @default.
• W1994064251 cites W2037302874 @default.
• W1994064251 cites W2044459812 @default.
• W1994064251 cites W2052246187 @default.
• W1994064251 cites W2055514786 @default.
• W1994064251 cites W2060608702 @default.
• W1994064251 cites W2070766114 @default.
• W1994064251 cites W2073998304 @default.
• W1994064251 cites W2076466685 @default.
• W1994064251 cites W2078476201 @default.
• W1994064251 cites W2078936307 @default.
• W1994064251 cites W2081526790 @default.
• W1994064251 cites W2088874310 @default.
• W1994064251 cites W2092101894 @default.
• W1994064251 cites W2111072639 @default.
• W1994064251 cites W2113238782 @default.
• W1994064251 cites W2125848133 @default.
• W1994064251 cites W2130765257 @default.
• W1994064251 cites W2141695047 @default.
• W1994064251 cites W2141946202 @default.
• W1994064251 cites W2155482699 @default.
• W1994064251 cites W2165564619 @default.
• W1994064251 doi "https://doi.org/10.1016/j.jweia.2013.03.005" @default.
• W1994064251 hasPublicationYear "2013" @default.
• W1994064251 type Work @default.
• W1994064251 sameAs 1994064251 @default.
• W1994064251 citedByCount "26" @default.
• W1994064251 countsByYear W19940642512014 @default.
• W1994064251 countsByYear W19940642512015 @default.
• W1994064251 countsByYear W19940642512016 @default.
• W1994064251 countsByYear W19940642512017 @default.
• W1994064251 countsByYear W19940642512018 @default.
• W1994064251 countsByYear W19940642512019 @default.
• W1994064251 countsByYear W19940642512020 @default.
• W1994064251 countsByYear W19940642512021 @default.
• W1994064251 countsByYear W19940642512022 @default.
• W1994064251 countsByYear W19940642512023 @default.
• W1994064251 crossrefType "journal-article" @default.
• W1994064251 hasAuthorship W1994064251A5024548236 @default.
• W1994064251 hasAuthorship W1994064251A5025107878 @default.
• W1994064251 hasAuthorship W1994064251A5035720793 @default.
• W1994064251 hasAuthorship W1994064251A5060780612 @default.
• W1994064251 hasAuthorship W1994064251A5065414106 @default.
• W1994064251 hasAuthorship W1994064251A5072580033 @default.
• W1994064251 hasConcept C11413529 @default.
• W1994064251 hasConcept C119599485 @default.
• W1994064251 hasConcept C119857082 @default.
• W1994064251 hasConcept C124101348 @default.
• W1994064251 hasConcept C127413603 @default.
• W1994064251 hasConcept C143724316 @default.
• W1994064251 hasConcept C151406439 @default.
• W1994064251 hasConcept C151730666 @default.
• W1994064251 hasConcept C153294291 @default.
• W1994064251 hasConcept C154945302 @default.
• W1994064251 hasConcept C161067210 @default.
• W1994064251 hasConcept C199360897 @default.
• W1994064251 hasConcept C205649164 @default.
• W1994064251 hasConcept C2777904410 @default.
• W1994064251 hasConcept C2780009758 @default.
• W1994064251 hasConcept C2780150128 @default.
• W1994064251 hasConcept C41008148 @default.
• W1994064251 hasConcept C45374587 @default.
• W1994064251 hasConcept C50644808 @default.
• W1994064251 hasConcept C78600449 @default.
• W1994064251 hasConcept C86803240 @default.
• W1994064251 hasConceptScore W1994064251C11413529 @default.
• W1994064251 hasConceptScore W1994064251C119599485 @default.
• W1994064251 hasConceptScore W1994064251C119857082 @default.
• W1994064251 hasConceptScore W1994064251C124101348 @default.
• W1994064251 hasConceptScore W1994064251C127413603 @default.
• W1994064251 hasConceptScore W1994064251C143724316 @default.
• W1994064251 hasConceptScore W1994064251C151406439 @default.
• W1994064251 hasConceptScore W1994064251C151730666 @default.
• W1994064251 hasConceptScore W1994064251C153294291 @default.
• W1994064251 hasConceptScore W1994064251C154945302 @default.
• W1994064251 hasConceptScore W1994064251C161067210 @default.
• W1994064251 hasConceptScore W1994064251C199360897 @default.

• next