This study presents a novel methodology for assessing wind turbine energy production and applies it at the Princess Elisabeth Antarctica (PEA) station by combining in situ observations, ERA5(-Land) reanalysis data, and statistical corrections. Traditional Quantile Mapping (QM) requires an overlap between observational and reanalysis data, limiting its application in practice. Parameterized Quantile Mapping (PQM) addresses this by using an exponential mapping function derived from the cumulative distribution function (CDF) relationship between observed and ERA5(-Land) data. This two-parameter function extends bias corrections beyond the calibration period, enabling the generation of accurate wind speed time series over longer timescales, even in data-scarce regions like Antarctica. The novelty of PQM lies in its ability to provide consistent, physically realistic corrections for wind data in data-scarce locations. By allowing bias corrections to be applied across unmeasured periods, we show PQM improves the accuracy of wind speed and power estimates at turbine hub height. This method improves the modeling of wind energy potential, enabling more reliable assessments of turbine performance in polar environments. PQM provides a flexible and robust solution for future wind resource evaluations in regions with limited observational data, making it a key tool for long-term wind energy assessments, as well as atmospheric boundary layer studies in the data-scarce Antarctic. The ERA5-Land data corrected by PQM provides the most accurate assessment of wind energy for the research station, with an annual average production error of 12%.