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Analysis of Variability and Uncertainty in Wind 

Power Forecasting: An International Comparison

Jie Zhang,1 Bri-Mathias Hodge,1 Jari Miettinen,2 Hannele Holttinen,2 Emilio GómezLázaro,3 Nicolaos Cutululis,4 Marisciel Litong-Palima,4 Poul Sorensen,4

Anne Line Lovholm,5 Erik Berge,5 and Jan Dobschinski6

October 22–24, 2013

12th International Workshop on Large-Scale Integration 

of Wind Power into Power Systems

London, United Kingdom

1National Renewable Energy Laboratory, Golden, CO, USA

2VTT Technical Research Centre of Finland, Espoo, Finland

3University of Castilla-La Mancha, Albacete, Spain

4Technical University of Denmark, Roskilde, Denmark

5Kjeller Vindteknikk AS, Kjeller, Norway

6Fraunhofer Institute for Wind Energy and Energy System Technology, Kassel, Germany

NREL/PR-5D00-60480
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Wind Power Forecasting

 One of the critical challenges of wind power integration is the

variable and uncertain nature of the resource.

 Short-term forecasting of wind power generation is uniquely

helpful for balancing supply and demand in the electric power

system, thereby reducing economic costs and reliability risks.

 Wind forecasting models can be broadly divided into two

categories: (i) forecasting based on the analysis of historical time

series of wind; and (ii) forecasting based on numerical weather

prediction (NWP) models.

 It is important to understand the nature of wind power forecast

errors, especially for large and infrequent forecast errors that can

dramatically impact system costs and reliability.
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Research Motivation and Objectives

• Investigate the uncertainty in wind forecasting at different times of year.

• Compare wind forecast errors in different power systems using largescale wind power prediction data from six countries: the United States,

Finland, Spain, Denmark, Norway, and Germany.

 Understanding forecast errors and uncertainties in different power

systems and scenarios is helpful for:

 Developing improved wind forecasting technologies, and

 Better allocating resources to compensate for wind forecast errors.

Motivation

Research Objectives
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Wind Forecasting Scenarios

 Hourly day-ahead wind power forecast errors throughout a year

 Forecast errors at a specific time of day throughout a year

 Hour 14:00 in this paper

 Forecast errors at peak and off-peak hours of the day

 Peak hours: 7:00 – 22:00

 Off-peak hours: 23:00 – 6:00

 Forecast errors during different seasons

 Summer and winter

 Extreme events: large overforecast or underforecast errors

 More than 25% of wind forecast errors are normalized by total wind capacity

 Forecast errors when the current wind power generation was at

different percentages of the total wind capacity

 Less than 25% of the total wind capacity

 Between 25% and 75% of the total wind capacity

 More than 75% of the total wind capacity
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Methodology Development

 Kernel density estimation (KDE): estimates the distribution of

wind power forecast errors

 The Gaussian kernel, , is used.

 Rényi entropy: quantifies the uncertainty in wind forecast errors

 Large values of α favor high probability events, whereas small values of α

weight all of the instances more evenly.

 Heat maps: allow the operator to simultaneously see the timing

and magnitude of forecast errors

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Data Summary

• Wind power forecast errors:

• The 1-hour-ahead forecasts for the six countries were synthesized using a 1-

hour-ahead persistence approach.

• The day-ahead forecasts were estimated using different methodologies for the

six countries.

w Pwf Pwa e = −

Country Year Capacity (MW) Wind Power Plants

United States (ERCOT) 2010 9,000 Concentrated

Finland 2012 130 23 wind plants

Spain 2011 14,000 Well dispersed

Denmark 2012 3,265 Well dispersed

Norway 2011 284 4 wind plants

Germany 2010 26,000 Well dispersed
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Distributions at Different Wind Power Levels

United States Finland Spain

Denmark Norway Germany
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Distributions at Different Hours

United States Finland Spain

Denmark Norway Germany
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Distributions of Extreme Wind Forecast Errors

United States Finland Spain

Denmark Norway Germany
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Distributions During Different Seasons (Summer and Winter)

United States Finland Spain

Denmark Norway Germany
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95th Percentiles of 1-Hour-Ahead Forecast Errors

United States Finland Spain

Denmark Norway Germany
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Heat Map of Day-Ahead Forecast Errors

United States Finland Spain

Denmark Norway Germany
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• According to the Rényi entropy metric, wind forecasting in the Danish power system

maintains a relatively lower level of uncertainty for most scenarios.

• According to the standard deviation, there is the least uncertainty in the forecasting for the

German power system, followed by the Danish power system.

• Power systems (Denmark, Germany, and Spain) with low forecast error variability have a

significant amount of well-dispersed wind power.

• For most power systems, forecasts in winter generally had more uncertainty than forecasts in

summer.

Comparison of Uncertainty in Wind Forecasting
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Concluding Remarks

 This paper compared the variability and uncertainty in wind power

forecasts for multiple power systems from six countries.

 Multimodal characteristics were observed in the extreme overforecast scenarios in

the Danish and Norwegian systems, and in the high-power scenario in the

German system.

 The distribution of forecast errors in the German power system was relatively

narrower than that in other countries.

 For most power systems, more underforecast events were observed in the highpower scenario than in the low- and medium-power scenarios.

 Maximum “up” and “down” reserves were required when actual wind

power generation was at medium to high percentages of the total wind

capacity.

 There was generally less uncertainty in forecasting when wind power

plants were dispersed throughout a wide geographic area.
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• This research was supported by the U.S. Department of Energy under

Contract No. DE-AC36-08-GO28308 with the National Renewable

Energy Laboratory.

Acknowledgement

• Investigate multiple years of wind forecasting data to obtain a

general trend of forecast errors.

• Compare the different methodologies in the forecasting systems

in different countries and seek to identify the possible sources of

bias and errors in the forecasts.

Future Work

Future Work
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Questions?