【摘要】 针对传统风电功率预测精度较差的问题，提出一种基于变模态分解(variational mode decomposition, VMD)、信息获取优化(information acquisition optimizer, IAO)算法和核极限学习机(kernel extreme learning machine, KELM)相组合的方法对超短期风电功率进行预测。采用VMD将非线性较强的原始功率数据分解为较为稳定的子序列；运用反向差分变异机制、适应度-距离平衡(fitness-distance balance, FDB)策略和天鹰俯冲策略改进IAO算法，得到改进信息获取优化(improved information acquisition optimizer, IIAO)算法并对KELM的正则化系数和核函数参数寻优；构建VMD-IIAO-KELM组合预测模型，将各子序列预测值叠加得到最终结果。算例分析结果表明，相比于普通核极限学习机模型的预测结果，本文所提模型的均方根误差下降了9.15%,平均绝对百分比误差和平均绝对误差分别下降了41.51%和12.56%,提高了超短期风电功率的预测精度。更多还原

【Abstract】 Addressing the challenge of low prediction accuracy associated with traditional wind power forecasting methods, a novel integrated approach was proposed, which combined variational mode decomposition(VMD), the information acquisition optimizer(IAO), and the kernel extreme learning machine(KELM) for ultra-short-term wind power forecasting.The original power data, characterized by high nonlinearity, were decomposed into more stable sub-sequences utilizing the VMD technique. The information acquisition optimizer(IAO) algorithm was enhanced through the introduction of a reverse differential mutation mechanism, a fitness-distance balance(FDB) strategy, and an eagle diving strategy, culminating in the development of the improved IAO(IIAO) algorithm. The regularization parameter and the kernel function parameter of the KELM were meticulously optimized using the IIAO algorithm. An integrated prediction model, referred to as VMD-IIAO-KELM, was constructed, with the final predictions derived from the aggregation of the predicted values of each sub-sequence.Results from the case study reveal that the proposed VMD-IIAO-KELM model achieves a reduction in the root mean square error by 9.15%, the mean absolute percentage error by 41.51%, and the mean absolute error by 12.56% in comparison to the conventional KELM model. It is concluded that the application of the proposed model significantly enhances the prediction accuracy of ultra-short-term wind power.更多还原