The effect of averaging, sampling, and time series length on wind power density estimations

Markus Gross*, Vanesa Magar, Alfredo Peña

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

32 Downloads (Pure)

Abstract

The Wind Power Density (WPD) is widely used for wind resource characterization. However, there is a significant level of uncertainty associated with its estimation. Here, we analyze the effect of sampling frequencies, averaging periods, and the length of time series on the WPD estimation. We perform this analysis using four approaches. First, we analytically evaluate the impact of assuming that the WPD can simply be computed from the cube of the mean wind speed. Second, the wind speed time series from two meteorological stations are used to assess the effect of sampling and averaging on the WPD. Third, we use numerical weather prediction model outputs and observational data to demonstrate that the error in the WPD estimate is also dependent on the length of the time series. Finally, artificial time series are generated to control the characteristics of the wind speed distribution, and we analyze the sensitivity of the WPD to variations of these characteristics. The WPD estimation error is expressed mathematically using a numerical-data-driven model. This numerical-data-driven model can then be used to predict the WPD estimation errors at other sites. We demonstrate that substantial errors can be introduced by choosing too short time series. Furthermore, averaging leads to an underestimation of the WPD. The error introduced by sampling is strongly site-dependent.
Original languageEnglish
Article number3431
JournalSustainability (Switzerland)
Volume12
Issue number8
Number of pages13
ISSN2071-1050
DOIs
Publication statusPublished - 2020

Keywords

  • Wind power density
  • Wind speed time series
  • Site characterization
  • Numerical-data-driven model

Fingerprint Dive into the research topics of 'The effect of averaging, sampling, and time series length on wind power density estimations'. Together they form a unique fingerprint.

Cite this