Ultra-short-term power load forecasting based on dynamic weighting mixture of experts

ZHOU Zhuan; WANG Jie; BIAN Jiayu; YU Zhiyong; YUAN Tiejiang

doi:10.11930/j.issn.1004-9649.202506007

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Ultra-short-term power load forecasting based on dynamic weighting mixture of experts

更新时间：2026-08-04

- Ultra-short-term power load forecasting based on dynamic weighting mixture of experts
- Vol. 59, Issue 5, Pages: 118-132(2026)
- 作者机构：
  
  1. 国网新疆电力有限公司,乌鲁木齐,830000
  2. 大连理工大学电气工程学院,大连,116081
  3. 国网新疆电力有限公司经济技术研究院,乌鲁木齐,830063
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202506007
  CLC：
- Online First：15 May 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
周专, 王杰, 边家瑜, 于志勇, 袁铁江. 基于动态权重混合专家模型的超短期电力负荷预测[J]. 中国电力, 2026, 59(5): 118-132.

ZHOU Zhuan, WANG Jie, BIAN Jiayu, et al. Ultra-short-term power load forecasting based on dynamic weighting mixture of experts[J]. 2026, 59(5): 118-132.
周专, 王杰, 边家瑜, 于志勇, 袁铁江. 基于动态权重混合专家模型的超短期电力负荷预测[J]. 中国电力, 2026, 59(5): 118-132. DOI： 10.11930/j.issn.1004-9649.202506007.

ZHOU Zhuan, WANG Jie, BIAN Jiayu, et al. Ultra-short-term power load forecasting based on dynamic weighting mixture of experts[J]. 2026, 59(5): 118-132. DOI： 10.11930/j.issn.1004-9649.202506007.

摘要

超短期电力负荷预测是新型电力系统实时调度的关键支撑技术，其精度直接决定新能源消纳能力、机组组合经济性及储能系统充放效率。针对负荷数据的强时序性、气象敏感性、日期敏感性及异常波动挑战，提出动态权重混合专家模型（dynamic weight-mixture of experts，DW-MoE）用于超短期负荷预测。首先，该模型通过双向长短期记忆网络（bi-directional long short-term memory，BiLSTM）捕捉负荷序列的周期性时序特征，借助极端梯度提升树（extreme gradient Boosting，XGBoost）刻画气象因子、日期因子与负荷的非线性关联，利用生成对抗网络（generative adversarial networks，GAN）实现异常负荷模式的精准检测。然后，设计基于滑动窗口误差反馈的动态权重机制，实现多专家输出的自适应融合。最后，引入在线更新机制，基于最新采样数据对模型参数进行增量式优化，提升模型对非平稳负荷波动的动态响应能力。实验结果表明，相较于单一模型及传统混合方法，DW-MoE模型在超短期电力负荷预测精度和收敛速度上均表现出明显优势，尤其在异常负荷场景中预测误差降低显著，验证了模型对突变负荷模式的鲁棒性。

Abstract

Ultra-short-term power load forecasting is a key supporting technology for real-time scheduling of new-type power systems

and its accuracy directly determines the consumption capacity of new energy

the economics of units combination

and the charging and discharging efficiency of energy storage system. To address the challenges of load data—including its strong temporal dependency

sensitivity to meteorological conditions

sensitivity to calendar effects

and anomalous fluctuations

a dynamic weighting mixture of experts (DW-MoE) model is proposed for ultra-short-term load forecasting. Firstly

the model captures the periodic temporal characteristics of the load sequences through BiLSTM

characterizes the nonlinear correlation between meteorological factors

date factors and loads using XGBoost

and achieves accurate detection of abnormal load patterns using GAN. Then

a dynamic weighting mechanism based on sliding window error feedback is designed to achieve adaptive fusion of multiple expert outputs; Finally

an online update mechanism is introduced to incrementally optimize the model’s parameters based on the latest sampled data

enhancing the dynamic response capability of the model to non-stationary load fluctuations. The experimental results demonstrate that compared to single models and traditional hybrid methods

the DW-MoE model exhibits significant advantages in both prediction accuracy and convergence speed for ultra-short-term load forecasting

and notably

it achieves a substantial reduction in prediction error under anomalous load scenarios

validating the model's robustness to abrupt load variations.

关键词

Keywords

references

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