Here a regime-switching vector autoregressive (VAR) method for very short-term wind speed forecasting (1-6 hours ahead) at multiple locations with regimes based on large-scale meteorological phenomena is presented.
Principal component analysis is first performed on surface wind, sea-level pressure fields and the geopotential height field at 500hPa level from MERRA-2 reanalysis dataset. Self-organising maps followed by k-means clustering is then used to group the data into atmospheric modes. Three atmospheric modes are found to be optimal for the case study of 6 years of measurements from 23 weather station in the UK. Mode 1 is associated with anticyclone circulation and moderate wind speed conditions, mode 2 is associated with low-wind speed cases and calm conditions over the UK and mode 3 is linked with cyclonic atmosphere circulation patterns and relatively high wind speed conditions (Figure 1.). Relatively small changes in large-scale atmospheric circulation may lead to different surface wind fields over the UK, which is important for wind energy applications.
VAR is used to capture advantages in using lagged measurements for spatially dispersed sites.
A range of VAR models is tested:
VAR_d – time of day is included as dummy variables as wind speed exhibits diurnal seasonality.
VAR_d_m – atmospheric mode dummies are also included.
CVAR_d – model parameters may themselves be dependent on atmospheric mode resulting in a conditional VAR model.
The RMSE for a 1 hour ahead forecast is reduced by 0.3% – 4.1% and for a 6 hour ahead forecast the improvement is about 3.1% compared to the most competitive benchmark. Improvement is dependent on the mode, the largest errors are associated with mode 3 (cyclonic conditions).
Relevancy to our research:
The authors suggest to run this method operationally the atmospheric mode could be determined from forecasts produced by NWP. They also note that the work here has only been applied to wind speed forecasting and further work is required to quantify the benefits for wind power forecasting. “Defining atmospheric modes on numerical weather predictions in order to forecast the future mode, for example, could enhance both very short-term and day-ahead wind and wind power forecasts.”