【摘要】 风电出力具有较强的随机性、波动性和间歇性，为保障新型电力系统下大电网的安全稳定运行，亟需高精度的短期风功率预测。充分利用风功率数据的时序特征，提出一种基于皮尔逊相关系数、变分模态分解和模糊熵的Stacking集成学习短期风功率预测方法。首先采用皮尔逊相关系数辨识主要气象变量；再使用变分模态分解将原始风功率序列分解成不同频率的子序列，运用模糊熵算法将各子序列划分为高低频子序列，分别建立适用于高低频率序列的Stacking集成学习短期风功率预测模型；最后经聚合重构获得最终预测结果。实际算例表明：与传统“分解-预测-分频-重构”模型对比，所提方法的平均绝对误差降低了6.0%～27.5%,显著提升了短期风功率预测的准确性。更多还原

【Abstract】 Wind power output exhibits strong randomness, volatility, and intermittency, and high-precision short-term wind power forecasting is urgently needed to ensure the safe and stable operation of the large power grid under the new power system. The time-series characteristics of wind power data were fully utilized, and a short-term wind power stacking learning ensemble forecasting method was proposed, based on pearson correlation coefficient, variational mode decomposition, and fuzzy entropy. First, the pearson correlation coefficient between wind power and meteorological variables was used to identify the main meteorological variables. Then, the wind power sequence was decomposed into subsequences using the variational mode decomposition algorithm. Fuzzy entropy values were used to classify subsequences into high-frequency or low-frequency subsequences, and stacking ensemble learning forecasting models suitable for high-frequency or low-frequency subsequences were established separately. Finally, the predictions were obtained through aggregation and reconstruction. Practical examples demonstrate that the proposed method reduces the mean absolute error by 6.0% to 27.5%, significantly improving the accuracy of short-term wind power forecasting.更多还原