Increasing Certainty - Combination methods for reliable wind production forecasts

Jeremy Parkes jeremy.parkes@gl-garradhassan.com

EWEA 2011, Tuesday 15 March 2011

Contents

• Background and general forecasting method• Why combine distributions to calculate forecast uncertainty?• Producing forecast distributions• Optimal combination of forecast distributions• Producing forecast power probability levels from combined distributions• Results• Conclusions

NWPForecast

GH Forecaster Current Forecasting Method

•Optimised combination of NWP suppliers• Incorporation of mesoscale models

•Regular live feedback from the wind farm•“Learning” Algorithms for:

• Meteorology• Power models

Suite of Models

Powermodel

Powerforecast

Modeladaptation

Modeladaptation

Wind speedforecast

HistoricSCADA

LiveSCADA

NWPForecastNWP

Forecast

Adaptive statistics ClimatologyTime Series

Intelligent Model Combination

LiveSCADA

Sitegeography

Sitegeography

Current Probabilistic Forecast

Hourly data 24 hours in advance Existing methods do not account for correlation of weather models

Why combine distributions?

• Accuracy of component forecasts for different meteorological conditions• Correlation of weather models

Calculating Forecast Distributions from Deterministic Wind Speed Forecasts

• Wind speed distributions assumed normal• Calculated from real wind speed data

Calculating Forecast Distributions from Ensemble Wind Speed Forecasts

• Ensemble member spread correlated to actual spread, but post-processing required

Optimal Combination of Forecast Distributions

• Optimal weightings via Normal Model[1]• Covariance of errors of forecast distributions

forecast in point oferror

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1. Clemen RT, Winkler RL. Combining probability distributions from experts in risk. Risk Analysis 1999; 19:187-203.

• Distribution combination• Forecast distribution correlation matrix

(Pearson coefficient)

onsdistributiforecast of dev. st.

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Forecast Power Probability Levels from Distributions

• Model inputs: • Forecast wind speed distribution• Power model for central estimate• Required probability level

• Transform wind speed distribution via power model Power distribution

Results - Example Probabilistic Forecast

Results - Probability Level Accuracy

P-Level Power Forecast Exceedance

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 12 24 36 48 60 72

Forecast Horizon (hrs)

Exc

eed

ance

(%

)

P10

P25

P50

P75

P90

Conclusions

• Knowledge of forecast uncertainty is important for decision makers (e.g. for energy traders, grid operators)• Ensemble post-processing is necessary to give accurate distributions• Multi-model ensembles provide the best probabilistic power forecasts• Distribution combination methods reflect correlation of multiple weather

models, and are sensitive to different weather conditions• Over short periods of time, combination of distributions gives more reliable

probabilistic wind production forecasts than previous methods

Any questions?jeremy.parkes@gl-garradhassan.com

See us at stand 7521/7529 Hall 7

Authors:Beatrice Greaves, Jonathan Collins, Jeremy Parkes, Lars Landberg