基于Transformer-集成学习的配电网短期负荷预测方法

张怀天; 贾东梨; 王帅; 何开元; 任昭颖; 刘佳静; 胡雪凯

doi:10.11930/j.issn.1004-9649.202511063

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基于Transformer-集成学习的配电网短期负荷预测方法

有源配电网安全高效运行与协同调控关键技术 | 更新时间：2026-08-04

- 基于Transformer-集成学习的配电网短期负荷预测方法
- Short-term load forecasting method for distribution networks based on transformer and ensemble learning
- 中国电力 2026年59卷第5期页码：33-45
- 作者机构：
  
  1. 中国电力科学研究院有限公司,北京,100192
  2. 国网河北省电力有限公司,河北,石家庄,050011
- 作者简介：
- 基金信息：
  
  智能电网重大专项（2030）资助项目（2025ZD0804600）。
- DOI：10.11930/j.issn.1004-9649.202511063
  中图分类号：
- 网络首发：2026-05-15，
  
  纸质出版：2026
- 稿件说明：
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张怀天, 贾东梨, 王帅, 何开元, 任昭颖, 刘佳静, 胡雪凯. 基于Transformer-集成学习的配电网短期负荷预测方法[J]. 中国电力, 2026, 59(5): 33-45.

ZHANG Huaitian, JIA Dongli, WANG Shuai, et al. Short-term load forecasting method for distribution networks based on transformer and ensemble learning[J]. 2026, 59(5): 33-45.
张怀天, 贾东梨, 王帅, 何开元, 任昭颖, 刘佳静, 胡雪凯. 基于Transformer-集成学习的配电网短期负荷预测方法[J]. 中国电力, 2026, 59(5): 33-45. DOI： 10.11930/j.issn.1004-9649.202511063.

ZHANG Huaitian, JIA Dongli, WANG Shuai, et al. Short-term load forecasting method for distribution networks based on transformer and ensemble learning[J]. 2026, 59(5): 33-45. DOI： 10.11930/j.issn.1004-9649.202511063.

摘要

在新型电力系统背景下，配电网分布式能源渗透率不断提高，负荷特性日趋多元，传统短期负荷预测方法难以有效捕捉高维非线性时序特征。为此，提出一种基于Transformer-集成学习的短期负荷预测方法。首先，构建多维特征嵌入层，融合负荷时序、周期特征及环境变量；其次，采用多头自注意力机制建立跨时段动态关联，提取负荷的时空耦合特性；然后，设计分层随机化前馈网络，结合Dropout增强模型隐空间的多模态表征能力；最后，集成多个差异化Dropout模型，通过多次前向传播采样实现对预测不确定性的贝叶斯评估。实验结果表明，所提方法在预测精度与稳定性上均优于现有基准模型，可为配电网优化调度提供有效支持。

Abstract

Against the backdrop of the new power systems

the penetration rate of distributed energy resources in distribution networks is rising steadily

and the load characteristics are becoming increasingly diversified. Existing short-term load forecasting methods thus fail to effectively capture the high-dimensional nonlinear temporal characteristics of load data. To address this issue

this paper proposes a short-term load forecasting method for distribution networks based on Transformer and ensemble learning. First

a multi-dimensional feature embedding layer is constructed to fuse the temporal and periodic characteristics of loads as well as environmental variables. Second

a multi-head self-attention mechanism is adopted to establish dynamic cross-time interval correlations

thereby extracting the spatiotemporal coupling characteristics of loads accurately. Third

a hierarchical randomized feedforward network is designed

with the Dropout technique integrated to enhance the multimodal representation capability of the model’s latent space. Finally

multiple differentiated Dropout-based models are ensembled

and Bayesian evaluation of forecasting uncertainty is realized through sampling with multiple forward propagations. Experimental results demonstrate that the proposed method outperforms state-of-the-art benchmark models in both forecasting accuracy and stability

and can thus provide effective technical support for the optimal dispatching of distribution networks.

关键词

Keywords

references

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