A new technique for imputation of multivariate time series: application to an hourly wind dataset

CARLOS LÓPEZ and ELÍAS KAPLAN

Centro de Cálculo, Faculty of Engineering (11)

CC 30, Montevideo, Uruguay

internet: carlos.lopez@ieee.org

internet: elias@fing.edu.uy

Abstract:The techniques employed in the treatment of an hourly surface wind database during the development and calibration phases of an objective wind field interpolator model are presented. The model itself has been applied to estimate the regional wind energy resource creating a layer in a GIS environment.

The outlier detection phase is presented in a companion paper, and here the different techniques applied in order to imputate the missing values are described. The comparative results obtained with an hourly dataset of 15 years long are also presented. Two different problems have been simulated numerically: systematic missing values (i.e. at fixed hours) and non systematic ones.

Five different criteria were applied: imputation with the historical mean value; linear time interpolation within single station records; optimum interpolation (kriging) and the two newly developed Penalty Of the Principal Scores and linear Time Interpolation of the Principal Scores which considers all station records in a multivariate fashion; they prove to be the most accurate for this particular wind dataset. There is also some evidence of oversampling in time.

Presented at:

X Congresso Brasileiro de Meteorologia, Brasilia, 26-30 October, 1998

If you are still interested in it; here you have: THE TECHNICAL REPORT (.DOC) (34KB) or in .PDF format (176KB)

carlos.lopez@ieee.org