基于高斯混合分布模型的风电功率预测误差统计分析研究

针对风电功率预测误差的统计分析，研究了一种基于高斯混合模型的风电功率预测误差分布，采用期望最大化算法，从统计学角度分析了风电功率负荷预测误差数据，并且在理论上证明了该方法的合理性。该方法的优点在于，无论其统计分布是怎样的，所有风电功率预测误差的概率密度函数都可以使用高斯混合模型近似表示，然后进行适当的子模型削减。通过对高斯混合模型与其他各种统计分布模型的性能进行比较，证明了高斯混合模型在风电功率预测误差统计分析应用中的有效性。

Statistical Analysis of Wind Power Forecasting Errors Based on Gaussian Mixture Model

With regard to the statistical analysis of wind power prediction error， a wind power forecasting error distribution based on Gaussian mixture model is studied. The expectation maximization algorithm is used to analyze the wind power load forecasting error data from the statistical point of view，which is theoretically proved. The rationality and advantage of this method is that regardless of its statistical distribution，the probability density function of all wind power forecasting errors can be approximated by using Gaussian mixture model. Then， appropriate sub-model reductions are made. By comparing the performance of Gaussian mixture model with other statistical distribution models， the effectiveness of Gaussian mixture model in the statistical analysis of wind power forecasting error is proved.