Wind prediction enhancement by exploiting data non-stationarity

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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.
Original languageEnglish
Title of host publication2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)
Place of PublicationPiscataway, N.J.
Number of pages5
DOIs
Publication statusPublished - 17 Nov 2016
Event2nd IET International Conference on Intelligent Signal Processing - Kensington Close Hotel, London, United Kingdom
Duration: 1 Dec 20152 Dec 2015

Conference

Conference2nd IET International Conference on Intelligent Signal Processing
CountryUnited Kingdom
CityLondon
Period1/12/152/12/15

Keywords

  • wind forecasting
  • multicannel prediction
  • non-stationary filtering
  • adaptive signal processing

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