基于AMD-ICSA-SVM的超短期风电功率组合预测

针对风机出力的随机性、波动性和不确定性，提出了一种基于解析模态分解(AMD)和改进布谷鸟优化支持向量机(ICSA-SVM)参数的超短期风电功率组合预测方法。首先，利用解析模态分解将风功率序列分解为不同频率范围的分量，减小不同频率范围间的相互影响。然后针对各序列特点，采用改进布谷鸟方法分别寻找各自支持向量机的惩罚因子参数和核函数参数，以提高单个模型的预测精度。最后对预测结果进行叠加和误差分析。仿真算例表明，所提出的方法可以很好地跟踪风电功率的变化，有效地提高风电功率预测精度。

Combination ultra-short-term prediction of wind power based on AMD-ICSA-SVM

A combination ultra-short-term prediction method of wind power based on analytical mode decomposition (AMD) and improved cuckoo search algorithms optimized support vector machines (ICSA-SVM) is proposed to treat with the randomness, volatility and uncertainty of wind power. Firstly, the wind power is decomposed into components with different frequencies by using AMD to reduce the influence between different frequencies. Then, according to the characteristic of each sequence, different penalty parameters and kernel function parameters are found by using ICSA to improve the forecasting accuracy of single model. Finally, the prediction results are superimposed for error analysis. Simulation results show that the proposed strategy can track the change of wind power better and improve the forecasting accuracy of wind power effectively.