Improved very-short-term wind forecasting using atmospheric regimes

TY - JOUR

T1 - Improved very-short-term wind forecasting using atmospheric regimes

AU - Browell, J.

AU - Drew, D. R.

AU - Philippopoulos, K.

PY - 2018/5/25

Y1 - 2018/5/25

N2 - 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 out-perform 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 500hPa 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 three atmospheric modes are found to be optimal for forecast performance. The skill of one- to six-hour-ahead forecasts is improved at all sites compared to persistence and competitive benchmarks. Across the 23 test sites, one-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared to the best performing benchmark, and by an average of 1.6% over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.

AB - 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 out-perform 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 500hPa 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 three atmospheric modes are found to be optimal for forecast performance. The skill of one- to six-hour-ahead forecasts is improved at all sites compared to persistence and competitive benchmarks. Across the 23 test sites, one-hour-ahead root mean squared error is reduced by between 0.3% and 4.1% compared to the best performing benchmark, and by an average of 1.6% over all sites; the six-hour-ahead accuracy is improved by an average of 3.1%.

KW - forcasting

KW - vector autoregression

KW - wind speed

KW - atmospheric classification

UR - https://arxiv.org/abs/1705.07158

UR - https://onlinelibrary.wiley.com/journal/10991824

U2 - 10.1002/we.2207

DO - 10.1002/we.2207

M3 - Article

JO - Wind Energy

T2 - Wind Energy

JF - Wind Energy

SN - 1095-4244

ER -