Actions for Assessing Long-Term Wind Conditions by Combining Different Measure-Correlate-Predict Algorithms [electronic resource] : Preprint

Email RIS file
Bookmark
Report an Issue

Assessing Long-Term Wind Conditions by Combining Different Measure-Correlate-Predict Algorithms [electronic resource] : Preprint

Published
Washington, D.C. : Wind and Hydropower Technologies Program (U.S.), 2013.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
13 pages : digital, PDF file
Additional Creators
National Renewable Energy Laboratory (U.S.), Wind and Hydropower Technologies Program (U.S.), and United States. Department of Energy. Office of Scientific and Technical Information
Access Online

Availability

I Want It
I Want It

Finding items...

Restrictions on Access
Free-to-read Unrestricted online access
Summary
This paper significantly advances the hybrid measure-correlate-predict (MCP) methodology, enabling it to account for variations of both wind speed and direction. The advanced hybrid MCP method uses the recorded data of multiple reference stations to estimate the long-term wind condition at a target wind plant site. The results show that the accuracy of the hybrid MCP method is highly sensitive to the combination of the individual MCP algorithms and reference stations. It was also found that the best combination of MCP algorithms varies based on the length of the correlation period.
Report Numbers
E 1.99:nrel/cp-5500-57647
nrel/cp-5500-57647
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
08/01/2013.
"nrel/cp-5500-57647"
To be presented at the ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 4-7 August 2013, Portland, Oregon.
Zhang, J.; Chowdhury, S.; Hodge, B. M.; Messac, A.
Funding Information
AC36-08GO28308
View MARC record | catkey: 14988718