Improved very-short-term wind forecasting using atmospheric regimes

Browell, J. , Drew, D.R. and Philippopoulos, K. (2018) Improved very-short-term wind forecasting using atmospheric regimes. Wind Energy, 21(11), pp. 968-979. (doi: 10.1002/we.2207)

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We present a regime-switching vector autoregressive method for very short-term wind speed forecasting at multiple locations with regimes based on large-scale meteorological phenomena. Statistical methods for wind speed forecasting based on recent observations outperform numerical weather prediction for forecast horizons up to a few hours, and the spatio-temporal interdependency between geographically dispersed locations may be exploited to improve forecast skill. Here, we show that conditioning spatio-temporal interdependency on “atmospheric modes” derived from gridded numerical weather data can further improve forecast performance. Atmospheric modes are based on the clustering of surface wind and sea-level pressure fields, and the geopotential height field at the 5000-hPa level. The data fields are extracted from the MERRA-2 reanalysis dataset with an hourly temporal resolution over the UK; atmospheric patterns are clustered using self-organising maps and then grouped further to optimise forecast performance. In a case study based on 6 years of measurements from 23 weather stations in the UK, a set of 3 atmospheric modes are found to be optimal for forecast performance. The skill of 1- to 6-hour-ahead forecasts is improved at all sites compared with persistence and competitive benchmarks. Across the 23 test sites, 1-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared with the best performing benchmark and by an average of 1.6% over all sites; the 6-hour-ahead accuracy is improved by an average of 3.1%.

Item Type:Articles
Additional Information:Jethro Browell is supported by the University of Strathclyde's EPSRC Doctoral Prize, grant number EP/M508159/1. Daniel Drew is funded by a NERC NPIF fellowship, grant number NE/RE013276/1.
Glasgow Author(s) Enlighten ID:Browell, Dr Jethro
Authors: Browell, J., Drew, D.R., and Philippopoulos, K.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Wind Energy
ISSN (Online):1099-1824
Published Online:25 May 2018
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in Wind Energy 21(11): 968-979
Publisher Policy:Reproduced under a Creative Commons License
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