中国电力2026，Vol.59Issue(5)：33-45,13.DOI:10.11930/j.issn.1004-9649.202511063
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
摘要
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
短期负荷预测/Transformer/集成学习/Dropout策略/前向传播采样Key words
short-term load forecasting/Transformer/ensemble learning/Dropout/forward propagation sampling引用本文复制引用
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):33-45,13.基金项目
智能电网重大专项(2030)资助项目(2025ZD0804600). This work is supported by Smart Grid-National Science and Technology Major Project(No.2025ZD0804600). （2030）
