Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

Alexander Basse,Anselm Grötzner,Lukas Pauscher,Doron Callies

doi:10.5194/wes-6-1473-2021

Abstract

Abstract. 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 with regard to accuracy in mean, variance, and turbine energy production – namely, variance ratio (VR) and linear regression with residuals (LR). 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. All these reanalysis data sets showed an overpronounced annual course of wind speed (i.e., wind speeds too high in winter and too low in summer). However, despite the mathematical similarity of the two MCP methods, these errors in the data resulted in very different seasonal biases when either the VR or LR methods were used for the MCP calculations. In general, the VR method produced overestimations of the mean wind speed when measuring in summer and underestimations in the case of winter measurements. The LR method, in contrast, predominantly led to opposite results. An analysis of the bias in variance did not show such a clear seasonal variation. Overall, the variance error plays only a minor role for the accuracy in energy compared to the error in mean wind speed. Besides the experimental analysis, a theoretical framework is presented which explains these phenomena. This framework enables us to trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. In summary, three aspects are identified as the main influential factors for the seasonal biases in mean wind speed: (1) the (dis-)similarity of the real wind conditions on-site in correlation and correction period (representativeness of the measurement period), (2) the capability of the reference data to reproduce the seasonal course of wind speed, and (3) the regression parameter β1 (slope) of the linear MCP method. This theoretical framework can also be considered valid for different measurement durations, other reference data sets, and other regions of the world.

Highlights

An extensive measurement campaign generally constitutes an essential part of wind resource assessment and, of a successful wind energy project
Despite the mathematical similarity of the two MCP methods, these errors in the data resulted in very different seasonal biases when either the variance ratio (VR) or linear regression with residuals (LR) methods were used for the MCP calculations
Three aspects are identified as the main influential factors for the seasonal biases in mean wind speed: (1) thesimilarity of the real wind conditions on-site in correlation and correction period, (2) the capability of the reference data to reproduce the seasonal course of wind speed, and (3) the regression parameter β1 of the linear MCP method

Summary

Introduction

An extensive measurement campaign generally constitutes an essential part of wind resource assessment and, of a successful wind energy project. Inter-annual variations in wind speed are reported to vary by between 4 % and up to 10 % (e.g., Corotis, 1976; Justus et al, 1979; Klink, 2002), depending on the respective site; the measured wind data usually do not represent the long-term wind conditions This aspect becomes even more momentous when the energy in the wind is considered, which has been reported to vary by 6 % (Pryor et al, 2018) up to 20 % or even 30 % (Corotis, 1976; Albrecht and Klesitz, 2006; Pryor et al, 2006) from year to year. Reference data are needed, which should be available for a long-term period of one to two decades (Lackner et al, 2008; Carta et al, 2013; Liléo et al, 2013) and show a high degree of similarity to the measured wind data (e.g., a high correlation coefficient of measured and reference data)

Methods

Results

Conclusion