Forecasting wind for the
renewable energy market
Matt Pocernich
Research Applications Laboratory
National Center for Atmospheric Research
pocernic@ucar.edu
Why is forecasting wind hard?
 Turbulence
 Inherently stochastic process
 Issue of scales
 Consequence – attempts at improving a deterministic
forecasts have physical limitations.
ENVR Workshop - October 2010
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Results of Spectral Decomposition
(Rife, Davis, Liu 2004 MWR)
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Power
Needs of the customer –
a typical power curve
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Outline
 Components of a typical numerical weather prediction
system.
 Ensembles Forecasting system
 Methods of post processing
 Verification of wind forecasts
 Excitement to come
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Dynamic Integrated Forecast
TM
System - DICast
Ensemble Input + Dynamic Weighting + Bias Correction + Dynamic MOS = Optimized Forecast
Performance
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4-D Continuous Data Assimilation and Forecasts
Modified WRF/MM5:
Dx/Dt = ... + GW (xobs – xmodel )
All WMO/GTS
GOES
where x = T, U, V, Q, P1, P2 …
Radars
W is weight function
Weather observations
RTFDDA
Wind Prof
Regional-scale
NWP models
WRF / MM5
t
WRF/MM5
Cold
start
FDDA
Forecast
MESONETs
Etc.
ACARS
ENVR Workshop - October 2010
Yuabo Liu et al.
Farm Met
Wind Energy Prediction - R & D Workshop. 11 -12 May 2010
4/13/2015
© 2010 University Corporation for Atmospheric Research
Assimilation of Wind Farm
Data
Met Tower
wind spd/dir
Turbine hub
wind spd
Data QC
and
processing
Other met-tower
weather
Observations
Data combining
and reformat
All other
weather
Observations
WRF
RTFDDA
ENVR Workshop - October 2010
Yuabo Liu et al.
Wind Energy Prediction - R & D Workshop. 11 -12 May 2010
4/13/2015
© 2010 University Corporation for Atmospheric Research
NWP Forecast
Solve the equations that describe the
evolution of the atmosphere
We cannot solve the equations analytically:
•Discretize them. Horizontally and vertically
•Close the equations by parameterizing small/fast physical
processes.
Load Forecasting Workshop
22 March 2007
Load Forecasting Workshop
22 March 2007
Ensemble Forecasting
– a very vague term
 Random initial conditions
 Multi-physics model
 Multi-model (Poor man’s ensemble)
 Time lag ensemble
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same event, designed to
characterize uncertainty
 Observational error
 Model selection error
 Parameterization error
Wind Speed (mph)
 Multiple forecasts of the
Forecast Hour
centers and forecasting
companies
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Wind Speed (mph)
 Run at many weather
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Forecast Hour
Challenges with ensembles
 Tend to be under dispersive (not enough spread.)
 Calibration for both reliability and sharpness.
 Some methods include
 ensembleBMA (Chris Frayley + UW)
 quantile regression (Hopson)
 ensemble Kalman Filter (more later from Luca)
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Ensemble BMA
 Bias removal of each member using linear regression.
 Estimates weights and variance for each ensemble member
which minimizes continuous rank probability score.
 Essentially, dresses each ensemble member with a
distribution.
 Traditionally uses Gaussian distribution. For winds, use
gamma.
 Key work by Adrian Raftery, Tilman Gneiting, MacLean
Sloughter and Chris Frayley (UW).
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Example of ensemble BMA
forecasts
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Regime switching Algorithms
(From M. Hering)
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ENVR Workshop - October 2010
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Key Verification Issues
 The most common verification metrics are mean
absolute error, RMSE and Bias.
 These do not address concerns like ramping events.
 New statistical forecasts are created every 15 minutes
with new physical model runs every 3 hours. We don’t
have a developed concept or metrics for consistency.
 Forecast value – cost/benefits is complicated. Value of
weather forecast is used with load forecast. There are
humans in the loop.
ENVR Workshop - October 2010
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Contingency table statistics
 The most fundamental verification methods involve statistics derived
from a contingency table. This requires forecasts and observations be
categorized into discrete bins.
 Basic contingency table statistics include hit rate, false positive rate,
bias, false negative rate and percent correct.
 Changes in power can be classified in such a way in the following
manner.
 An increase (or decrease) in a forecast accompanied by a “similar” increase
(or decrease) in observed power is a good forecast.
 A change forecast in power, but not observed is a false positive.
 A change observed, but not forecast is a false negative
 A forecast of no-change, associated with no change is considered a good,
negative forecast.
 The definition of a good forecast can be modified. Regions do not have
to be defined by angular regions.
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Forecast vs. Observed
Changes
Agree in magnitude and
direction
Disagree in magnitude and
direction
Small values forecast and
observed
False Positive and False
Negative
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Regions translated into a
contingency table
Forecast
Observed
Up Ramp
Neutral
Down Ramp
Up Ramp
Neutral
Down Ramp
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Changes in power forecast by short term
forecast from in the first 3 hours
Forecast
Observed
Up
Down
Neutral
Ramp
Ramp
Up
Ramp
970
322
228
Neutral
573
149
603
Down
Ramp
300
435
696
 Percent Correct 42%
 Gerrity Skill Score 0.24
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0 hour lead time, 1- 3 hour
duration
GSS = 0.21
PC = 48%
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GSS = 0.21
PC = 39%
GSS = 0.24
PC = 42%
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Criticisms of this approach
From C. Ferro - U.Exeter
 The classification of the observation as either neutral,
down ramp or up ramp depends on the value of the
forecast. That seems weird! It must lead to some
difficulties in interpreting any analysis of the table.
 Much easier to define categories using boundaries that
are parallel to the observation and forecast axes.
 My initial reaction to deal with this is not to use
contingency tables at all but to model the continuous
joint distribution.
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More data + better data = more
fun
 New instruments –
LIDAR and SODAR
 Better quality
observations from
existing stations.
 Improvements in sharing
data.
 High quality networks of
tall towers. (BPA)
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Space – Time processes?
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Concluding Remarks
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