基于CEEMDAN-ISSA-BiLSTM的风电功率组合预测模型

Wind Power Combination Prediction Model Based on CEEMDAN-ISSA-BiLSTM

针对风电功率存在间歇性、非线性和波动性而难以准确预测的问题,提出一种遵循“序列分解-网络预测-序列重构”的风电功率预测模型。针对风电场集群中的不同风电机组出力特性曲线,使用迭代自组织数据分析聚类算法(ISODATA)聚类得到典型出力曲线;利用自适应噪声完全集成经验模态分解(CEEMDAN)算法对聚类得到的原始风电序列数据进行模态分解,减少数据波动所带来的预测误差;建立各模态分量的双向长短期记忆网络(BiLSTM)预测模型,并使用改进麻雀搜索算法(ISSA)优化网络参数,再将各模态分量的预测结果叠加得到风电功率的最终预测结果。算例结果表明,所提预测模型的预测精度相比其他对比模型更高,且有着更好的泛化能力。

To solve the problem that wind power is difficult to predict accurately due to intermittent and nonlinear fluctuations,a wind power prediction model based on“sequence decomposition-network prediction-sequence reconstruction”is proposed.First,according to the output characteristic curves of various wind turbines in wind farm clusters,the typical output curves are obtained through Iterative Self Organizing Data Analysis Techniques Algorithm(ISODATA)clustering algorithm.Then,the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)is used to decompose the original wind power series data obtained by clustering to reduce the prediction error caused by data fluctuation.Third,the Bi-directional Long Short-Term Memory(BiLSTM)prediction model of each modal component is established,the Improved Sparrow Search Algorithm(ISSA)is used to optimize the network parameters,and the final prediction result of wind power is obtained by superimposing the prediction results of each modal component.The numerical results show that the prediction accuracy of the proposed model is higher than that of other models,and it has better generalization ability.