Wind Power Curve Modeling With Asymmetric Error Distribution

Wind power curves play important roles in developing and utilizing wind energy, including wind power forecasting, wind turbine condition monitoring, wind energy potential estimation, and wind turbine selection. In this paper, we show that the error distribution in wind power curve modeling is asymmetric and complex, rather than a Gaussian distribution. Considering this error characteristic, we develop two novel asymmetric spline regression models, MoAG-ASR and MoAEP-ASR, in which the error terms are assumed to obey mixture of asymmetric Gaussian distributions (MoAG) and mixture of asymmetric exponential power distribution (MoAEP), respectively. We compare the proposed models with 16 popular wind power curve fitting models, and find that the proposed models rank higher than the others. Moreover, besides the asymmetry, two statistics (skewness and kurtosis) also show that the error distribution in wind power curve modeling is thick-tailed and leptokurtic. However, MoAG and MoAEP can precisely fit such complex error distribution. Thus, MoAG-ASR and MoAEP-ASR are effective tools for wind power curve modeling.