基于高频增强扩散模型的短期净负荷概率预测

王城煌; 罗李子; 龙寰; 高润天; 王小明

doi:doi:10.19912/j.0254-0096.tynxb.2024-2184

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基于高频增强扩散模型的短期净负荷概率预测

更新时间：2026-07-22

- 基于高频增强扩散模型的短期净负荷概率预测
- Vol. 47, Issue 4, Pages: 103-113(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-2184
  CLC： TM714
- Online First：08 May 2026，
  
  Published：2026
- 稿件说明：
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王城煌, 罗李子, 龙寰, et al. 基于高频增强扩散模型的短期净负荷概率预测[J]. 2026, 47(4): 103-113.
DOI：

王城煌, 罗李子, 龙寰, et al. 基于高频增强扩散模型的短期净负荷概率预测[J]. 2026, 47(4): 103-113. DOI： doi:10.19912/j.0254-0096.tynxb.2024-2184.

摘要

为保障高比例清洁能源接入场景下电网的安全稳定运行

提出一种基于高频增强扩散模型的短期净负荷概率预测方法。首先

基于极限梯度提升（XGBoost）算法和最大信息系数（MIC）提取关键气象特征

形成多元输入条件特征集。其次

构建基于交叉注意力机制的条件扩散模型架构

并针对编码-解码环节

分别设计融合小波注意力的CNN-LSTM网络和多通道卷积神经网络

以增强对高频特征的捕捉能力。然后

基于初始预测值和核密度估计方法

利用改进的损失函数对预训练模型进行微调

以进一步提高生成区间的质量。最后

基于华东某地区电网实际负荷数据开展算例分析

并与多种模型进行对比

结果表明所提方法具有更高的预测精度。

Abstract

In order to ensure the safe and stable operation of the power grid under the scenario of high proportion of clean energy access

a short-term net load probability prediction method based on high frequency enhanced diffusion model is proposed. Firstly

key meteorological features are extracted based on the eXtreme Gradient Boosting（XGBoost） algorithm and maximum information coefficient（MIC）to form a multivariate input condition feature set. Secondly

a conditional diffusion model architecture based on cross-attention mechanism is constructed. For the encoding-decoding link

a CNN-LSTM network incorporating wavelet attention and a multi-channel convolutional neural network are designed respectively to enhance the ability to capture high-frequency features. Then

based on the initial predicted values and kernel density estimation methods

the pre-trained model is fine-tuned using an improved loss function to improve the quality of the generated intervals further. Finally

an example analysis is carried out based on the actual load data of a power grid in East China

and the results show that the proposed method has a higher prediction accuracy when compared with various models.

关键词

Keywords

references

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