基于双阶段参数优化多核核密度估计的风电功率区间预测方法

张素琪; 赵旺; 李天白; 李亚娟

doi:10.19912/j.0254-0096.tynxb.2025-0822

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基于双阶段参数优化多核核密度估计的风电功率区间预测方法

更新时间：2026-10-15

- 基于双阶段参数优化多核核密度估计的风电功率区间预测方法
- Vol. 47, Issue 9, Pages: 841-853(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0822
  CLC： TM614
- Online First：10 October 2026，
  
  Published：2026
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张素琪, 赵旺, 李天白, et al. 基于双阶段参数优化多核核密度估计的风电功率区间预测方法[J]. 2026, 47(9): 841-853.
DOI：

张素琪, 赵旺, 李天白, et al. 基于双阶段参数优化多核核密度估计的风电功率区间预测方法[J]. 2026, 47(9): 841-853. DOI： 10.19912/j.0254-0096.tynxb.2025-0822.

摘要

在风电功率预测中

预测误差分布具有多模态和非对称特性

易导致局部概率密度失真与区间估计偏差问题。针对上述问题

提出一种基于双阶段参数优化多核核密度估计的风电功率区间预测方法。首先

根据不同核函数的概率密度估计特性

通过加权融合多个不同核密度估计函数

解决单核核密度估计在多模态场景下的局部概率密度失真问题;其次

针对多核核密度估计中权重和带宽参数的高维非线性耦合优化难题

提出双阶段参数优化方法：第一阶段采用基于白鲸优化算法（BWO）的参数对分域搜索策略

对权重和带宽进行初步优化;第二阶段引入基于留一法交叉验证（LOOCV）的带宽细化调优方法

提升带宽的精度。最后

通过在公开数据集和现场实测数据集上进行实验

验证所提方法的误差概率密度拟合效果优于单核核密度估计方法

与现有区间预测方法相比

具有更优的预测性能。

Abstract

In wind power prediction

the multimodal and asymmetric characteristics of prediction error distributions often lead to local probability density distortion and interval estimation deviations. To address these challenges

this paper proposes a wind power interval prediction method based on dual-stage parameter optimized multiple kernel-based kernel density estimation. Firstly

by investigating the probability density estimation characteristics of different kernel functions

multiple kernel density estimation functions are weighted and fused to resolve the local probability density distortion issue of single-kernel kernel density estimation in multi-modal scenarios. Secondly

to tackle the high-dimensional nonlinear coupling optimization problem of weight and bandwidth parameters in multiple kernel-based kernel density estimation

a dual-stage parameter optimization framework is developed： In the first stage

a parameter-pair partitioned domain search strategy based on the beluga whale optimization （BWO） algorithm is proposed for preliminary optimization of weights and bandwidths. The second stage employs a bandwidth refinement tuning method utilizing leave-one-out cross validation （LOOCV） to improve bandwidth precision. Finally

experiments conducted on both public and field-measured datasets demonstrate that the proposed method outperforms single-kernel kernel density estimation in error probability density fitting. Furthermore

comparative analyses with existing interval prediction methods reveal its superior predictive performance.

关键词

Keywords

references

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