Wind prediction enhancement by exploiting data non-stationarity

Alice Malvaldi; Jethro Dowell; Stephan Weiss; David Infield

doi:10.1049/cp.2015.1795

Wind prediction enhancement by exploiting data non-stationarity

Alice Malvaldi, Jethro Dowell, Stephan Weiss, David Infield

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

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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 language	English
Title of host publication	2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)
Place of Publication	Piscataway, N.J.
Number of pages	5
DOIs	https://doi.org/10.1049/cp.2015.1795
Publication status	Published - 17 Nov 2016
Event	2nd IET International Conference on Intelligent Signal Processing - Kensington Close Hotel, London, United Kingdom Duration: 1 Dec 2015 → 2 Dec 2015

Conference

Conference	2nd IET International Conference on Intelligent Signal Processing
Country/Territory	United Kingdom
City	London
Period	1/12/15 → 2/12/15

Keywords

wind forecasting
multicannel prediction
non-stationary filtering
adaptive signal processing

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10.1049/cp.2015.1795

Malvaldi-etal-IET-ICISP-2015-Wind-prediction-enhancement-by-exploiting-dataAccepted author manuscript, 322 KB

Cite this

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title = "Wind prediction enhancement by exploiting data non-stationarity",

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{\textquoteright}s Met Office, we demonstrate the impact of this proposed approach.",

keywords = "wind forecasting, multicannel prediction, non-stationary filtering, adaptive signal processing",

author = "Alice Malvaldi and Jethro Dowell and Stephan Weiss and David Infield",

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AU - Malvaldi, Alice

AU - Dowell, Jethro

AU - Weiss, Stephan

AU - Infield, David

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PY - 2016/11/17

Y1 - 2016/11/17

N2 - 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.

AB - 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.

KW - wind forecasting

KW - multicannel prediction

KW - non-stationary filtering

KW - adaptive signal processing

U2 - 10.1049/cp.2015.1795

DO - 10.1049/cp.2015.1795

M3 - Conference contribution book

SN - 978-1-78561-136-0

BT - 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

CY - Piscataway, N.J.

T2 - 2nd IET International Conference on Intelligent Signal Processing

Y2 - 1 December 2015 through 2 December 2015

ER -

Wind prediction enhancement by exploiting data non-stationarity

Abstract

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Keywords

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