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Seasonal Effects in the Long-Term Correction of Short-Term Wind Measurements Using Reanalysis Data

: Basse, Alexander; Callies, Doron; Grötzner, Anselm; Pauscher, Lukas

Volltext urn:nbn:de:0011-n-6331313 (1.5 MByte PDF)
MD5 Fingerprint: 6271802414ba899e86080590de5ff053
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Erstellt am: 12.3.2021

Wind energy science : WES (2021), Online First, 29 S.
ISSN: 2366-7443
ISSN: 2366-7451
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
6. EFP; 0324159A; LiMeS
Optimierung von LiDAR basierten Messstrategien zur Standortbewertung von Windparks
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IEE ()
Measure-Correlate-Predict; Seasonal Bias; wind resource assessment; Variance Ratio; regression

Measure-Correlate-Predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared, namely Variance Ratio and Linear Regression with Residuals. Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. Besides experimental results, theoretical considerations are presented which provide the mathematical background for understanding the observations. General relationships are derived which trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. This allows the transfer of the results of this study to different measurement durations, other reference data sets and other regions of the world. In this context, it is shown both theoretically and experimentally that the results do not only depend on the selected reference data set but also significantly change with the choice of the MCP method.