Wind prediction enhancement by exploiting data non-stationarity

Malvaldi, Alice and Dowell, Jethro and Weiss, Stephan and Infield, David; (2016) Wind prediction enhancement by exploiting data non-stationarity. In: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP). IET, GBR. ISBN 978-1-78561-136-0 (https://doi.org/10.1049/cp.2015.1795)

Preview

Text. Filename: Malvaldi_etal_IET_ICISP_2015_Wind_prediction_enhancement_by_exploiting_data.pdf Accepted Author Manuscript Download (329kB)| Preview

Abstract

The short term forecasting of wind speed and direction has previously been improved by adopting a cyclo-stationary multichannel linear prediction approach which incorporat ed seasonal cycles into the estimation of statistics. This pap er expands previous analysis by also incorporating diurnal va ri- ation and time-dependent window lengths. Based on a large data set from the UK’s Met Office, we demonstrate the impact of this proposed approach.

ORCID iDs

Malvaldi, Alice ORCID: https://orcid.org/0000-0003-4378-9024

Dowell, Jethro ORCID: https://orcid.org/0000-0002-5960-666X

Weiss, Stephan ORCID: https://orcid.org/0000-0002-3486-7206

• Share and Export
  

  RefWorksRIS (EndNote Online, Zotero, Ref manager, etc)RDF+XMLEndNoteASCII CitationOpenURL ContextObject in SpanMPEG-21 DIDLRIOXX2 XMLBibTeXAtomEP3 XMLDublin CoreRDF+N-TriplesMODSHTML CitationJSONData Cite XMLRDF+N3Multiline CSVOpenURL ContextObjectReferSimple MetadataMETS
• Item metadata

  Item type:	Book Section
  ID code:	60149
  Dates:	Date Event 17 November 2016 Published 22 June 2015 Accepted
  Notes:	This paper is a postprint of a paper submitted to and accepted for publication in 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP) and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering Technology and Innovation Centre > Sensors and Asset Management
  Depositing user:	Pure Administrator
  Date deposited:	13 Mar 2017 11:27
  Last modified:	17 Nov 2024 01:28
  URI:	https://strathprints.strath.ac.uk/id/eprint/60149