Wind prediction enhancement by supplementing measurements with numerical weather prediction now-casts

A. Malvaldi; J. Dowell; S. Weiss; D. Infield; D. Hill

Wind prediction enhancement by supplementing measurements with numerical weather prediction now-casts

A. Malvaldi, J. Dowell, S. Weiss, D. Infield, D. Hill

Research output: Contribution to conference › Paper

64 Downloads (Pure)

Abstract

This paper explores how the accuracy of short-term prediction of wind speed and direction can be enhanced by considering additional spatial measurements. To achieve this, two different data sets have been used: (i) wind speed and direction measurements taken over 23 Met Office weather stations distributed across the UK, and (ii) outputs from the Consortium for Small-scale Modelling (COSMO) numerical weather model on a grid of points covering the UK and the surrounding sea. A multivariate complex valued adaptive prediction filter is applied to these data. The study provides an assessment of how well the proposed model can predict the data one hour ahead and what improvements can be accomplished by using additional data from the COSMO model.

Original language	English
Pages	1-4
Number of pages	4
Publication status	Published - Oct 2014
Event	10th EAWE PhD Seminar on Wind Energy in Europe - l'Ecole Polytechnique de l'Université d'Orléans, Orléans, France Duration: 28 Oct 2014 → 31 Oct 2014 http://eawephdseminar.sciencesconf.org/

Conference

Conference	10th EAWE PhD Seminar on Wind Energy in Europe
Country/Territory	France
City	Orléans
Period	28/10/14 → 31/10/14
Internet address	http://eawephdseminar.sciencesconf.org/

Keywords

wind energy
wind forecasting
multichannel adaptive filter

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

Malvaldi-etal-EAWE-2016-Wind-prediction-enhancementFinal published version, 355 KB

Cite this

@conference{a0f296ada9194d5b90a9e47ecf99e58f,

title = "Wind prediction enhancement by supplementing measurements with numerical weather prediction now-casts",

abstract = "This paper explores how the accuracy of short-term prediction of wind speed and direction can be enhanced by considering additional spatial measurements. To achieve this, two different data sets have been used: (i) wind speed and direction measurements taken over 23 Met Office weather stations distributed across the UK, and (ii) outputs from the Consortium for Small-scale Modelling (COSMO) numerical weather model on a grid of points covering the UK and the surrounding sea. A multivariate complex valued adaptive prediction filter is applied to these data. The study provides an assessment of how well the proposed model can predict the data one hour ahead and what improvements can be accomplished by using additional data from the COSMO model.",

keywords = "wind energy, wind forecasting, multichannel adaptive filter",

author = "A. Malvaldi and J. Dowell and S. Weiss and D. Infield and D. Hill",

year = "2014",

month = oct,

language = "English",

pages = "1--4",

note = "10th EAWE PhD Seminar on Wind Energy in Europe ; Conference date: 28-10-2014 Through 31-10-2014",

url = "http://eawephdseminar.sciencesconf.org/",

}

TY - CONF

T1 - Wind prediction enhancement by supplementing measurements with numerical weather prediction now-casts

AU - Malvaldi, A.

AU - Dowell, J.

AU - Weiss, S.

AU - Infield, D.

AU - Hill, D.

PY - 2014/10

Y1 - 2014/10

N2 - This paper explores how the accuracy of short-term prediction of wind speed and direction can be enhanced by considering additional spatial measurements. To achieve this, two different data sets have been used: (i) wind speed and direction measurements taken over 23 Met Office weather stations distributed across the UK, and (ii) outputs from the Consortium for Small-scale Modelling (COSMO) numerical weather model on a grid of points covering the UK and the surrounding sea. A multivariate complex valued adaptive prediction filter is applied to these data. The study provides an assessment of how well the proposed model can predict the data one hour ahead and what improvements can be accomplished by using additional data from the COSMO model.

AB - This paper explores how the accuracy of short-term prediction of wind speed and direction can be enhanced by considering additional spatial measurements. To achieve this, two different data sets have been used: (i) wind speed and direction measurements taken over 23 Met Office weather stations distributed across the UK, and (ii) outputs from the Consortium for Small-scale Modelling (COSMO) numerical weather model on a grid of points covering the UK and the surrounding sea. A multivariate complex valued adaptive prediction filter is applied to these data. The study provides an assessment of how well the proposed model can predict the data one hour ahead and what improvements can be accomplished by using additional data from the COSMO model.

KW - wind energy

KW - wind forecasting

KW - multichannel adaptive filter

UR - https://eawephdseminar.sciencesconf.org/

M3 - Paper

SP - 1

EP - 4

T2 - 10th EAWE PhD Seminar on Wind Energy in Europe

Y2 - 28 October 2014 through 31 October 2014

ER -

Wind prediction enhancement by supplementing measurements with numerical weather prediction now-casts

Abstract

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this