Determining the ideal length of wind speed series for wind speed distribution and resource assessment

Abstract

Abstract. Accurate wind resource assessment depends on wind speed data that capture local wind conditions, which are crucial for energy yield estimates and site selection. While the International Electrotechnical Commission (IEC) recommends at least 1 year of data collection, this duration may be insufficient to fully capture interannual variability. Although studies often maximize data length, limited guidance exists on the minimum sample size required to reliably estimate wind statistics and energy potential. To address this gap, we propose a method to quantify errors in wind speed distribution parameters arising from the use of time series of varying lengths compared with long-term reference data. This enables us to determine the minimum number of hourly observations needed to achieve a given accuracy. We apply this method to in situ station observations and ERA5 reanalysis data at 10 and 100 m heights. Our results show that basic parameters (mean, standard deviation, and Weibull parameters) stabilize with a sample size equivalent to ∼ 1 month of hourly data (not a contiguous period) drawn across multiple years, while higher-order moments require substantially larger samples (skewness: equivalent to ∼ 1.6 years; kurtosis: equivalent to 88.6 years). Although ERA5 stabilizes faster, it exhibits systematic biases compared to in situ measurements. Moreover, random cross-year sampling yields comparable distribution parameters to diurnally or seasonally controlled sampling, while continuous sampling demands far longer records for the same accuracy. These findings provide a practical framework for optimizing data collection in wind resource assessments, balancing accuracy, temporal coverage, and resource constraints.