Abstract Details
Activity Number:
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623
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Type:
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Invited
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Date/Time:
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Thursday, August 7, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #310912
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View Presentation
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Title:
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Improving Wind Speed and Direction Forecasts by Combining Process and Stochastic Spatio-Temporal Models
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Author(s):
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Petrutza Caragea and Lisa Bramer*+ and Mark Kaiser
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Companies:
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Iowa State University and PNNL and Iowa State University
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Keywords:
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wind forecasting ;
wind direction ;
spatial MRF
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Abstract:
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Wind energy has undergone rapid growth in recent years. The current goal of the Department of Energy to have 20% of the nation's electrical energy from wind by 2030 will require continued rapid growth. Wind, however, unlike other sources of energy, varies greatly over space and time, such that the production rates of energy can fluctuate much more strongly than with most traditional sources of energy generation. To best take advantage of wind for power generation, accurate forecasts are needed.
The spatial modeling of wind speed is complicated by the role played by wind direction, topography, and the geography of wind turbines within one farm, leading to wake effects and turbulence in the wind field. We propose several competing conditionally specified Markov random field (MRF) models for wind speed, with some models incorporating wind direction. We discuss several statistical criteria for the assessment of many of the practical decisions regarding the statistical application of MRF models to hourly wind data in the Midwestern U.S.
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Authors who are presenting talks have a * after their name.
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