A short-term ensemble wind-speed forecasting system for wind power applications

Traiteur, Justin J.

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https://hdl.handle.net/2142/24081 Copy

Description

Title

A short-term ensemble wind-speed forecasting system for wind power applications

Author(s)

Traiteur, Justin J.

Issue Date

2011-05-25T14:57:57Z

Director of Research (if dissertation) or Advisor (if thesis)

Roy, Somnath B.

Department of Study

Atmospheric Sciences

Discipline

Atmospheric Sciences

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Short-term wind speed forecasting
• Bayesian Model Averaging
• Weather Research and Forecasting Single-Column Model (WRF-SCM)

Abstract

Accurate short-term wind speed forecasts for utility-scale wind farms will be crucial for the U.S. Department of Energy’s (DOE) goal of providing 20% of total power from wind by 2030. For typical pitch-controlled wind turbines, power output varies as the cube of wind speed over a significant portion of the power output curve. Therefore, small improvements in wind-speed forecasts would constitute much larger improvements in wind power forecasts. In addition, communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate forecasting system is developed. This system uses a 21-member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM V3.1.1) to generate a probability distribution function (PDF) of 1-hour forecasts at a 90m height location in West/Central Illinois. The WRF-SCM ensemble was initialized by the 20 km Rapid update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The PDF was calibrated using Bayesian Model Averaging (BMA) where the individual forecasts were weighted according to their performance. This combination of a mesoscale numerical weather prediction ensemble system and Bayesian statistics allowed for both accurate prediction of 1-hour wind speed forecasts and their level of uncertainty.

Graduation Semester

2011-05

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http://hdl.handle.net/2142/24081

Copyright and License Information

Copyright 2011 Justin J. Traiteur

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