NREL is a national laboratory of the U.S. Department of Energy, Office of Energy 

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Enhanced Short-Term Wind 

Power Forecasting and Value 

to Grid Operations

Preprint

K. Orwig

National Renewable Energy Laboratory

C. Clark and J. Cline

U.S. Department of Energy

S. Benjamin, J. Wilczak, and M. Marquis

National Oceanic and Atmospheric Administration

C. Finley

WindLogics

A. Stern

National Weather Service

J. Freedman

AWS Truepower

To be presented at the 11th Annual International Workshop on LargeScale Integration of Wind Power into Power Systems as Well as on 

Transmission Networks for Offshore Wind Power Plants Conference

Lisbon, Portugal

November 13–15, 2012

Conference Paper

NREL/CP-5500-56258

September 2012
NOTICE

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1

Enhanced Short-Term Wind Power Forecasting and 

Value to Grid Operations

The Wind Forecasting Improvement Project

Kirsten D. Orwig

Transmission Grid Integration

National Renewable Energy Laboratory

Golden, CO USA

kirsten.orwig@nrel.gov

Stan Benjamin

James Wilczak

Melinda Marquis

Earth System Research Laboratory

National Oceanic and Atmospheric Administration

Boulder, CO USA

Andrew Stern

National Oceanic and Atmospheric Administration

Silver Spring, MD USA

Charlton Clark

Joel Cline

Wind and Water Power Program

U.S. Department of Energy

Washington, DC USA

Catherine Finley

WindLogics

Grand Rapids, MN USA

Jeffrey Freedman

AWS Truepower

Albany, NY USA

Abstract—The current state-of-the-art wind power forecasting in 

the 0- to 6-h timeframe has levels of uncertainty that are adding 

increased costs and risks to the U.S. electrical grid. It is widely 

recognized within the electrical grid community that 

improvements to these forecasts could greatly reduce the costs 

and risks associated with integrating higher penetrations of wind 

energy. The U.S. Department of Energy has sponsored a 

research campaign in partnership with the National Oceanic and 

Atmospheric Administration (NOAA) and private industry to 

foster improvements in wind power forecasting. The research 

campaign involves a three-pronged approach: (1) a one-year 

field measurement campaign within two regions; (2) 

enhancement of NOAA’s experimental 3-km High-Resolution 

Rapid Refresh (HRRR) model by assimilating the data from the 

field campaign; and (3) evaluation of the economic and 

reliability benefits of improved forecasts to grid operators. This 

paper and presentation provide an overview of the regions 

selected, instrumentation deployed, data quality and control, 

assimilation of data into HRRR, and preliminary results of 

HRRR performance analysis.

Keywords-forecast; numerical weather prediction; economic 

value; measurements; grid operations; wind variability

I. INTRODUCTION

A number of wind energy integration and other studies [1–

3] have shown that wind power forecasting can provide 

significant economic and reliability benefits for grid operators;

however, these studies have focused only on day-ahead 

forecasts. The potential value of shorter term forecasts has not 

been explored.

The uncertainty of wind power forecasts also has an 

impact on the potential value to the user. Errors in the forecast 

could pose substantial economic and reliability risks, 

particularly for grid operators and market participants. Current 

state-of-the-art (SOA) wind power forecasting has 

uncertainties that range from 10% to 15% mean absolute error

MAE (e.g., [4]). Uncertainties in the forecast can stem from 

poor spatial resolution, inadequate boundary layer physics, 

model drift, data assimilation technique, wind speed to power 

conversion, etc.

Many meteorologists in and outside of the energy industry 

believe that better and more widespread measurements could 

significantly reduce the uncertainty of current SOA forecasts. 

This argument stems from the recognition that most nearsurface meteorological measurements assimilated into 

numerical weather prediction (NWP) models are from below 

the wind turbine hub height and that few observations resolve 

the full depth of the boundary layer and the lower troposphere 

up to ~3km depth.

Therefore, the DOE sponsored a partnership with the 

National Oceanic and Atmospheric Administration (NOAA), 

private industry, and academia to answer the following 

questions:

1. Do additional measurements lead to better forecasts?

2. Do modified NWP models improve power forecasts?

3. What value do grid operators get from better shortterm (0- to 6-h) power forecasts?

The U.S. Department of Energy Wind and Water Power Program, in 

partnership with the National Oceanic and Atmospheric Administration, 

sponsored the work presented here.
2

This research campaign, called the Wind Forecasting 

Improvement Project (WFIP), began in October 2010 and 

consists of two project teams (Tables 1 and 2). Both projects 

are being conducted in three overlapping phases. The first 

phase is a one-year field campaign; the second phase examines 

the value of additional measurements in the NWP and power 

forecasts; and the third phase assesses the operational and 

economic benefits of the improvements. The DOE, Lawrence 

Livermore National Laboratory, Pacific Northwest National 

Laboratory, and Argonne National Laboratory provided 

supplemental instrumentation, technical support, and analyses. 

This paper discusses the three phases for each project in detail, 

with focus on the field campaign and NWP enhancements. 

Additional details from the southern study region can be found 

in two accompanying papers: (1) “The Wind Forecasting 

Improvement Project: Description and Results from the 

Southern Study Region” by J. Freedman et al. [5] and (2)

“Economic Evaluation of Short-Term Wind Power Forecasts 

in ERCOT: Preliminary Results” by K. Orwig et al. [6].

TABLE I. SOUTHERN STUDY REGION LED BY AWST

Team Members Field 

Campaign Forecasts

Power 

System 

Analysis

NOAA X X

AWS Truepower X X

MESO X

NC State University X X

OK University X X

TX Tech University X X

ICF International X

NREL X

ERCOT X

TABLE II. NORTHERN STUDY REGION LED BY WINDLOGICS

Team Members Field 

Campaign Forecasts

Power 

System 

Analysis

NOAA X X

WindLogics X X

NextEra X

SD State University X

NREL X

MISO X

II. FIELD CAMPAIGN

The field campaign began in August 2011 and concludes

in September 2012. During this period, NOAA has been

assimilating the data collected into their NWP models, 

discussed in Section III.

A. Northern Study Region

The northern study is led by WindLogics and covers a 

large portion of the Midwest Independent System Operator

(MISO) service area (Fig. 1). There is extensive wind energy 

development within this region, and the meteorological 

conditions are representative of the broader Great Plains,

where the majority of wind energy development is taking 

place. Seven 915-Mhz Radio Acoustic Sounding System 

(RASS) profilers (new); 2 449-Mhz RASS profilers (new); 5 

sodars (new); 39 tall towers (existing); 3 surface flux (new);

and approximately 400 nacelle anemometers were utilized for 

assimilation into NOAA’s NWP models.

B. Southern Study Region

The southern study is led by AWS Truepower (AWST)

and covers most of the Electricity and Reliability Council of 

Texas (ERCOT) service area (Fig. 2). As of March 2012, 

ERCOT had 9,838 MW of wind capacity installed and is 

seeing instantaneous presentations up to 24% of the load [7].

Figure 1. Northern study region and instrumentation.

Figure 2. Southern study region and instrumentation.
3

For WFIP, new measurement systems were utilized,

including 2 915-Mhz RASS profilers (new); 7 sodars (new); 3

surface flux stations (new); a 200-m instrumented tower and

915-Mhz RASS profiler at Texas Tech University’s Reese 

Technology Center near Lubbock, Texas (existing); and 34

wind plant tall towers within the ERCOT domain (only 15 of 

which ended up providing reliable data).

III. FORECAST ENHANCEMENT

A. Numerical Weather Prediction Models

NOAA’s NWP models are the foundation for the wind 

power forecasts generated by private industry providers such 

as WindLogics and AWST, the industry team leads for the 

two WFIP projects. When WFIP began, the SOA in 

operational meteorological forecasts produced by NOAA was 

the 13-km resolution Rapid Update Cycle (RUC) model

(which is no longer operational). The domain covers the area 

in black in Fig. 3. The RUC generated new forecasts every 

hour for 0- to 18-h horizons [8].

Figure 3. NOAA NWP domains: 13-km RAP in blue, retired RUC 

domain in black, 3-km HRRR in green, and study regions in red [9].

1) The Rapid Refresh Model

The National Centers for Environmental Prediction 

(NCEP) Rapid Refresh (RAP) model went operational on 

May 1, 2012, replacing the RUC model. The NCEP RAP, like 

the RUC, has a 13-km resolution, but it covers a larger 

domain and has several significant differences [10]. These

differences include: 

• The Weather Research and Forecasting (WRF) 

model v3.2.1+ with rapid refresh capabilities as the 

model core, replacing the RUC forecast model;

• A terrain-following vertical coordinate system 

(referred to as the sigma vertical coordinate system);

• A rotated latitude-longitude projection grid; and

• A rapid refresh version of the Gridpoint Statistical 

Interpolation (GSI) analysis system for updating 

model initial fields with recent observations.

A second version of the RAP, which is currently 

experimental, is the NOAA Earth Systems Research 

Laboratory (ESRL) version of the model [10]. The ESRL 

RAP has some additional modifications. These modifications

include:

• WRF v3.3.1+ as the model core;

• Improved physics;

• MODIS land-surface data including fractional 

coverage;

• A newer version of the GSI system; 

• Improved assimilation techniques, especially in the 

near-surface layer; and

• The assimilation of new data collected within the 

two study areas, as well as other data.

Additionally, the ESRL RAP is used to initialize the HighResolution Rapid Refresh (HRRR) model, discussed in more 

detail in the next section.

2) High Resolution Rapid Refresh

The HRRR model is an experimental, 3-km resolution, 

hourly updated NWP model [11]. It is based on the WRF

v3.3.1, and its configuration is similar to that of the ESRL 

RAP, but it does not include a convective parameterization. 

Improvement in HRRR performance is strongly dependent on 

the enhancements made to the ESRL RAP and the data 

assimilation process.

B. Data Denial Study

To evaluate how new measurements impact the HRRR 

and ESRL RAP performance, retrospective simulations will 

be run with and without the new data for the same time 

periods. Each of the study areas will be examined, and a 

detailed meteorological analysis of ramp events will be 

conducted. This analysis will be performed in fall 2012.

C. Wind Power Forecasts

1) Northern Study Region

WindLogics is taking a three-pronged approach by (1) 

utilizing the raw forecast, (2) generating a trained forecast 

using Support Vector Machine methods, and (3) generating a 

trained ensemble forecast. Power forecasts are then generated 

using each of these approaches for each of three models, the 

NCEP RAP, ESRL RAP, and the HRRR. The raw forecasts 

are generated by using a bias corrected model wind speed and 

a derived plant power curve to create a power forecast. The 

trained ensemble forecast is a combination of the output of 

each of the trained models individually with the operational 

North American Mesoscale model.
4

2) Southern Study Region

AWST currently supplies ERCOT with a Short-Term 

Wind Power Forecast (STWPF). This forecast is used as a 

baseline for this study. The new experimental forecast 

consists of an ensemble of high-resolution rapid update NWP 

models. Each of these ensemble members incorporates a 

variety of model configurations, physics parameterizations, 

and data assimilation techniques. The purpose of integrating 

all of these ensemble members into one product is to 

construct an optimized composite forecast to be able to

predict forecast uncertainty and assess the relative 

performance of different modeling approaches. The ensemble 

members include (Fig. 4):

• NOAA’s 3-km HRRR updated hourly;

• Nine NWP models updated every 2 h on a 5-km grid

run by MESO:

o Three configurations of Advanced Regional 

Prediction System (ARPS);

o Three configurations of WRF;

o Three configurations of Mesoscale Atmospheric 

Simulations System; and

• Oklahoma University’s version of ARPS updated 

every 6 h on a 2-km grid

The data from additional sensors deployed for this project as 

well as the data from a set of participating wind farms within 

Texas were assimilated into most of the ensemble members;

however, the data from the project sensors were withheld 

from some ensemble members to gauge their impact on the 

forecasts.

A Model Output Statistics (MOS) procedure was applied 

to the forecasts from each NWP system. The MOS is 

designed to correct systematic errors of relevant NWP 

meteorological variables (e.g., wind speed and direction) at 

forecast sites (i.e., the wind farms). Several MOS strategies 

based on variations of sample selection strategies and 

statistical prediction tools were used to generate an ensemble 

of statistical predictions from each NWP system. The MOS 

output for the individual NWP systems was then used as input 

to an Optimized Ensemble Model (OEM), which created a 

composite deterministic or probabilistic forecast from the set 

of MOS-adjusted NWP forecasts. In addition to the NWP 

forecasts, statistical predictions based purely on recent 

observational data were also included in the ensemble. Two 

OEM training strategies were tested: one was based on a 

rolling sample of the last 30 days; a second approach was 

based on a customized “analog” training sample that was 

constructed by matching key weather parameters for the 

current forecast period with those for cases in a historical 

archive. The objective of the regime-based approach was to 

weight the individual members of the ensemble according to 

their performance in weather patterns that were similar to the 

one expected during the forecast period.

The resultant ensemble forecast was then converted to a 

power generation forecast at 15-min intervals out to 6 h. More 

details of AWST’s forecasting system can be found in [5].

IV. FORECAST PERFORMANCE EVALUATION

A. Metrics Development

A key part of WFIP is evaluating the performance of the 

atmospheric and wind power forecasts. Generally, the 

industry has used MAE or Root Mean Square Error (RSME) 

Figure 4. Schematic of AWST wind power forecasting system. 
5

as standard metrics for forecast performance. One major 

drawback of these metrics, though, is that they assume the 

errors have a normal or Gaussian distribution. Researchers at 

the National Renewable Energy Laboratory (NREL) have 

been investigating nonparametric methods to characterize the 

errors, such as information entropy and hyperbolic-based 

methods [12]. The development and utilization of these new 

metrics for performance evaluation is still underway. 

Preliminary evaluations using the current standard metrics 

and qualitative comparisons of the forecasts will be discussed 

further here.

B. Wind Speed Forecast Performance Verification

1) Northern Study

The evaluation of the wind speed forecast performance for 

the northern study is still underway. The evaluation will be 

conducted using current standard metrics, such as MAE and 

RSME, but will also be evaluated using the new metrics 

being developed. 

2) Southern Study

Forecast performance is being extensively analyzed. This 

includes an evaluation of each physics-based and statistical 

component of the forecast system as well as the performance 

of “final” ensemble composite forecasts. It also includes an 

evaluation of the variations in forecast performance by time 

of year, time of day, weather regime, and other factors.

Preliminary results from December 2011 (Fig. 6) show the 

comparative MAEs among the ensemble members, 

persistence, and the new ensemble forecast. The ensemble 

forecast performs the best, with ~10% lower MAE than most 

of the ensemble members individually. More details can be 

found in [5].

C. Wind Power Forecast Performance Verification

1) Northern Study

The evaluation of the wind power forecast is ongoing;

however, preliminary results are showing some improvement 

in MAE between the NCEP RAP, ESLR RAP, and HRRR. 

Fig. 7 shows an example of this improvement with the 

percent of the normalized difference in MAE between the 

RUC and HRRR (red) and the NCEP RAP and HRRR (blue) 

for all forecast horizons out to 12 h. For all sites, HRRR 

performs better than the RUC; and for most sites, HRRR 

performs better than the NCEP RAP.

2) Southern Study

The evaluation of the wind power forecast performance 

for the southern study is also ongoing. Similar to the northern 

study, though, preliminary results show that the new 

ensemble-based power forecast outperformed the power 

forecasts generated by single members of the ensemble or the 

current operational ERCOT STWPF forecast (Fig. 8). More 

details are included in [6].

V. ECONOMIC EVALUATION

The final phase of WFIP is to evaluate the benefits of 

improved wind power forecasts for MISO and ERCOT. This 

evaluation involves determining the impact on what was 

delivered versus scheduled, imbalance costs and penalties, 

market participation, ancillary service costs, and potential 

emissions implications.

A. Midwest Independent System Operator

To determine the potential benefits for MISO, NREL 

researchers are using PLEXOS [14], a sub-hourly production 

cost model. Coal, nuclear, and large oil and gas generating 

units are committed on 15-h-ahead wind generation forecasts. 

Figure 5. Hub height wind speed forecast MAE for December 2011 for 

the southern study [13].

Figure 6. Percent difference of normalized MAE for wind power 

forecasts at various sites in northern study.

Figure 7. Percent MAE for the current SOA versus the new 

experimental forecast. [13]
6

Combined cycle and combustion turbines are committed 

based on short-term wind power forecasts. Additionally, 

MISO has a Dispatchable Intermittent Resource (DIR) 

program in place, which is a mechanism for variable 

generators, such as wind generators, to participate in the 

market. The benefits of the forecasts will be evaluated both 

with and without the DIR system.

B. Electric Reliability Council of Texas

The wind power forecasts generated by the AWST team 

will be used to evaluate the benefits of any forecast 

improvements to ERCOT. The production cost model used 

was GE-MAPS (MAPS) [15], which simulated the power 

market on an hourly resolution, committing and dispatching 

generators to meet load by optimizing the production cost. A 

base case was run simulating the current ERCOT system. A 

gas sensitivity case was also run to evaluate how the results 

would change for various gas price scenarios. Preliminary 

results are available for the first six months of forecasts and 

are presented in [6]. Alternative reserve scenarios will also be 

evaluated.

VI. CONCLUSION

The goal of the DOE/NOAA/industry partnership is to 

advance wind power forecasting by demonstrating the benefit 

of measurement networks and enhanced NOAA foundational 

NWP products, and ultimately show the potential value of 

those improvements to end users—in this case, the energy 

industry. Preliminary results are promising and show that 

significant reductions in wind power forecast errors are 

possible and can provide substantial value to the grid.

ACKNOWLEDGMENTS

This work was supported by the U.S. Department of 

Energy under Contract No. DE-AC36-08-GO28308 with the 

National Renewable Energy Laboratory.

The authors would also like to thank the National Oceanic 

and Atmospheric Administration for their sponsorship and 

support.

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