The WRF mode was used to investigate the prediction of wind speed ramping events which can have a large impact on renewable energy production.
5 years of forecast data was used for this analysis. 2 sets of deterministic forecasts (10km and 5km resolutions) produced by downscaling GFS 0.5° data and an ensemble forecast produced by downaling the 21 members of the GEFS 1.0° data. The area studied here are 6 coastal stations in northwest Japan. Wind speed ramps are considered changes of greater than or equal to ±5m/s in 6 hours. The ability of the forecast to predict these events as binary occurrences (“yes/no events”) was examined. A variety of skill scores were utilised to measure forecast performance such as Probability Of Detection, Success Rate, Frequency Bias, Probability Of False Detection, Critical Success Index.
The higher resolution deterministic forecasts outperform the coarser resolution forecasts. Ramp ups are also predicted better than ramp down events. The use of probability thresholds from the set of ensemble forecasts offered the ability to predict wind ramp events better.
The WRF forecast model was used to downscale GFS driving data with domains (shown below) going from 9km to 3km to 1km (two-way nested with 34 levels) and an inner domain of 333m resolution (one-way nested with 67 levels) around a windfarm in Galicia (NW Spain). The MYNN2.5 PBL scheme was used and a year of simulations were produced (with and without the Fitch wind turbine wake parametrization scheme).
Forecasts were compared to both nacelle wind speed measurements and wind power output for 31 different turbines in this particular wind farm. The validation indicated that the output from the 333m domain did offer enhanced prediction compared to the coarser domain but the Fitch scheme did not offer any improvement over the forecasts run without the scheme.
Plots of the difference between the two sets of simulations (such as above) highlight the wind speed losses produced due to turbine wakes by the Fitch scheme indicating that it is indeed having an impact at curbing the wind speeds, even though it is not producing a better forecast.
Here analog models were used to obtain daily mean wind speed
and wind gust estimations in Spain. Three datasets were used: daily 1000hPa geopotential
height field over the North Atlantic at 12:00 UTC (Z1000) from ERA40 reanalysis,
observational daily mean wind speed (MWS) and observational daily gust wind
speeds (WGU) in Spain. Principal component analysis is used to reduce the
dimensionality of the large-scale atmospheric pattern before the analog method
is applied. The analog method is based on finding a PC subset of large-scale atmospheric
patterns in the historic geopotential height that are most similar to a
large-scale atmospheric pattern used as input.
In any analog model a weighting function is needed that considers the similarity of a situation to the past situations. Here, two Euclidean metrics are defined to be used in the ANPAF (Analog Pattern Finder) analog model (Figure 1.). One which takes into account the full set of PCs and another which considers a truncated set of PCs. The search of analog patterns is based on finding a time t that minimizes such distances in the PCA space. The result is a measure of the RMS distance between the historic PC score and the input PC score (the input atmospheric pattern (ie. the forecast)). d1 is the measure using all PCs and d2 is the measure using the elements of the retained PCs, avoiding some part of variability not contained in the retained first patterns and supposed as noise. λj is the eigenvalue which gives a measure of the variance of the data and is included to weight the variability of the different retained PCs.
Once the similarity scores have been obtained their corresponding dates give the associated wind fields resulting in an estimated wind field over Spain by averaging the analogs. A sensitivity study is performed to choose the best analogs. Two strategies have been used: distance threshold and fixed analog number. Large distance thresholds can correspond to climatological estimations (for a distance threshold of 50, more than 3000 analogs are found). In some cases a distance threshold ends with missing results if no analogs similar enough are found. In order to avoid this, the second method of setting the number of analogs is used. It was found that between 5 and 10 was the best number of analogs to use (Figure 2.).
Relevancy to our research:
This method may be used in our estimation of the PCs most similar to our input forecasts in our post-processing method.
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.”