Wind Power Ultra-short-term Interval Prediction Based on an Adaptive Kernel Density Estimation Algorithm

GUO Hengkuan; TIAN Jianyan; LIU Shuwei; JIANG Yingying; YANG Lizhi

doi:10.19725/j.cnki.1007-2322.2024.0048

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Wind Power Ultra-short-term Interval Prediction Based on an Adaptive Kernel Density Estimation Algorithm

更新时间：2026-06-02

- Wind Power Ultra-short-term Interval Prediction Based on an Adaptive Kernel Density Estimation Algorithm
- Vol. 43, Issue 3, Pages: 499-508(2026)
- 作者机构：
  
  太原理工大学电气与动力工程学院,山西省,太原市,030024
- 作者简介：
- 基金信息：
- DOI：10.19725/j.cnki.1007-2322.2024.0048
  CLC： 3
- Published：2026
- 稿件说明：
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GUO Hengkuan, TIAN Jianyan, LIU Shuwei, et al. Wind Power Ultra-short-term Interval Prediction Based on an Adaptive Kernel Density Estimation Algorithm[J]. 2026, 43(3): 499-508.
DOI：

GUO Hengkuan, TIAN Jianyan, LIU Shuwei, et al. Wind Power Ultra-short-term Interval Prediction Based on an Adaptive Kernel Density Estimation Algorithm[J]. 2026, 43(3): 499-508. DOI： 10.19725/j.cnki.1007-2322.2024.0048.

摘要

针对风电功率区间预测中核密度估计算法局部适应性不强的问题，提出一种自适应核密度估计算法，实现风电功率超短期区间预测。首先，构建基于双重注意力机制改进序列到序列(sequence to sequence

Seq2Seq)架构的长短期记忆神经网络风电功率超短期预测模型，对风电功率进行确定性预测。然后，利用提出的一种自适应核密度估计算法，对风电功率预测误差数据集进行核密度估计，得到误差数据集的概率密度函数。最后，通过对概率密度函数进行积分，得到误差数据集的分布函数，并根据分布函数求得不同置信度下的上侧和下侧分位数，将分位数和风电功率确定性预测值求和，实现相应置信度下的风电功率超短期区间预测。基于中国西北某风电场的数据进行仿真预测分析，结果表明该自适应核密度估计算法对风电功率预测误差数据具有更强的适应能力，验证了该风电功率区间预测模型的有效性。

Abstract

Aiming at the issue of insufficient local adaptability in kernel density estimation algorithm for wind power interval prediction

we propose an adaptive kernel density estimation algorithm for wind power ultra-short-term interval prediction. Firstly

a long short term-memory wind power ultra-short-term prediction model is constructed based on the improved sequence to sequence (Seq2Seq) architecture with a dual-stage attention mechanism for wind power deterministic prediction. Subsequently

an proposed adaptive kernel density estimation algorithm is utilized to estimate the kernel density of the historical wind power prediction error. The proposed algorithm is capable of adaptively adjusting the bandwidth according to the kernel density estimation of the prediction error. Finally

the cumulative distribution function of the prediction error is obtained by integrating the kernel density estimation results. In addition

the upper and lower quantiles under different confidence coefficients are calculated

and the ultra-short-term prediction intervals of wind power under the corresponding confidence coefficients are obtained by adding the quantiles and the deterministic prediction values. Simulation analysis based on the data from a wind farm in Northwest China demonstrate that the proposed adaptive kernel density estimation algorithm exhibits superior adaptive ability to wind power prediction error data. Moreover

the effectiveness of this wind power interval prediction model is validated through these experiments.

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references

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