The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report-Reference-Cited by-同舟云学术

The Wind Forecast Improvement Project (WFIP): A Public/Private Partnership for Improving Short Term Wind Energy Forecasts and Quantifying the Benefits of Utility Operations. The Southern Study Area, Final Report

Published:2014-04-30 Issue: Volume: Page:
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Author:

Freedman Jeffrey M.¹,Manobianco John²,Schroeder John³,Ancell Brian⁴,Brewster Keith⁵,Basu Sukanta⁶,Banunarayanan Venkat⁷,Hodge Bri-Mathias⁸,Flores Isabel⁹

Affiliation:

1. AWS Truepower, LLC, Albany, NY (United States)

2. MESO, Inc., Troy, NY (United States)

3. Texas Tech Univ., Lubbock, TX (United States). National Wind Inst.

4. Texas Tech Univ., Lubbock, TX (United States). Atmospheric Science Group

5. Univ. of Oklahoma, Norman, OK (United States). Center for Analysis and Prediction of Storms

6. North Carolina State Univ., Raleigh, NC (United States). Dept. of Marine, Earth, and Atmospheric Sciences

7. ICF International (United States)

8. National Renewable Energy Lab. (NREL), Golden, CO (United States)

9. Electricity Reliability Council of Texas (United States)

Publisher

Office of Scientific and Technical Information (OSTI)

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1. MicroGrid Renewable Integration Dispatch and Sizing (MiGRIDS) Analysis of Spinning and Regulating Reserve Options for Wind in an Alaskan Diesel Microgrid;IEEE Access;2023

2. Weather, Climatic and Ecological Impacts of Onshore Wind Farms;Reference Module in Earth Systems and Environmental Sciences;2020

3. Measuring the impact of additional instrumentation on the skill of numerical weather prediction models at forecasting wind ramp events during the first Wind Forecast Improvement Project (WFIP);Wind Energy;2019-05-30

4. Minute-Scale Forecasting of Wind Power—Results from the Collaborative Workshop of IEA Wind Task 32 and 36;Energies;2019-02-21

5. A data-driven multi-model methodology with deep feature selection for short-term wind forecasting;Applied Energy;2017-03