基于Stacking融合的短期风速预测组合模型  

Combination Model of Short-term Wind Speed Prediction Based on Stacking Fusion

作　　者：李永刚[1] 王月 刘丰瑞 吴滨源 LI Yonggang;WANG Yue;LIU Fengrui;WU Binyuan(State Key Laboratory of New Energy Power System(North China Electric Power University),Baoding 071003,Hebei Province,China;School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,Jilin Province,China)

机构地区：[1]新能源电力系统国家重点实验室(华北电力大学),河北省保定市071003 [2]东北电力大学电气工程学院,吉林省吉林市132012

出　　处：《电网技术》2020年第8期2875-2882,共8页Power System Technology

基　　金：国家自然科学基金项目(51777075)。

摘　　要：大规模风电并网时,准确的风速预测对电网稳定运行具有重要意义。为提高风速预测精度及预测模型泛化能力,提出基于改进Stacking算法的风速组合预测模型。首先建立基于不同核函数的核岭回归模型;然后利用改进的萤火虫算法对模型关键参数进行选取,通过引入自适应参数、全局搜索及Levy飞行提高算法的全局搜索能力及收敛速度;最后通过Stacking算法将相互独立的各模型进行融合,以增强模型泛化性,并采用交叉验证进一步提高预测精度。选择不同风场、不同季节的实测数据对所提模型的预测效果进行仿真,通过对比分析验证了所提模型的预测精度和泛化能力。Accurate wind speed prediction is of great significance to the stable operation of the power grid when a large scale of wind power is connected into the grid. In order to improve the wind speed prediction accuracy and the model generalization ability, a combined wind speed prediction model based on improved Stacking algorithm is proposed. First, a kernel ridge regression model based on different kernel functions is established. Then key parameters are selected using an improved firefly algorithm. Improve the global search ability and convergence speed of the algorithm by introducing adaptive parameters, global search and Levy flight. Finally, the independent models are fused by the Stacking algorithm to enhance the generalization of the combined model, and the cross-validation is used to further improve the prediction accuracy. The measured results of different wind fields and in different seasons are selected to simulate the prediction effect of the proposed model, and its prediction accuracy and generalization ability are verified through comparative analysis.

关 键 词：风速预测 核岭回归 改进萤火虫算法 Stacking算法 交叉验证 

分 类 号：TM614[电气工程—电力系统及自动化]