中图分类号：TM715 文献标志码：A DOI: 10.16157/j.issn.0258-7998.257379 中文引用格式： 毛洪彬，舒征宇，雷明，等. 基于LASSO及GRU-注意力机制的配电网合环电流区间预测[J]. 电子技术应用，2026，52(6)：74-82. 英文引用格式： Mao Hongbin，Shu Zhengyu，Lei Ming，et al. Interval prediction of distribution network loop-closing current based on LASSO and GRU-attention mechanism[J]. Application of Electronic Technique，2026，52(6)：74-82.
Interval prediction of distribution network loop-closing current based on LASSO and GRU-attention mechanism
Mao Hongbin，Shu Zhengyu，Lei Ming，Ren Guanchen，Zhou Zihan
China Three Gorges University
Abstract： Against the backdrop of large-scale integration of distributed energy resources, such as photovoltaic and wind power, into the power grid, traditional methods for calculating loop-closing currents face challenges of insufficient accuracy and difficulty in characterizing the uncertainty of loop-closing characteristics. To address this, this paper proposes an interval prediction model for loop-closing currents that considers uncertainty. Specifically, the research process includes the following key steps. First, given the numerous influencing factors and complex data structure in loop-closing current prediction, this paper adopts the Adaptive LASSO (ALASSO) regression method to screen influencing factors and construct a multivariate dataset suitable for loop-closing current prediction. Second, to enhance the interpretability of the loop-closing current prediction model, the study introduces the Extreme Gradient Boosting algorithm to evaluate the feature importance of the influencing factors selected by LASSO, clarifying the contribution of each factor to the prediction results. Then, to address the temporal characteristics of loop-closing currents, this paper proposes a prediction model based on an attention-mechanized Gated Recurrent Unit (GRU) for time-period prediction of loop-closing currents. This model dynamically allocates weights through the attention mechanism to capture key features in the time-series data of loop-closing currents, thereby improving prediction accuracy. Finally, to more accurately represent the uncertainty of loop-closing current predictions, the study employs the Bootstrap method to scientifically calculate the confidence interval, thereby quantifying the potential fluctuation range of the prediction results. Subsequently, the calculated confid
Key words : adaptive LASSO；Bootstrap algorithm；attention mechanism；feature extraction；loop-closing current；uncertainty
为提升配电网合环电流预测的动态精度与泛化能力，本文提出一种融合注意力机制与混合神经网络的概率化预测模型。该模型采用三层技术架构：特征提取与权重分析、时序建模及不确定性量化。首先，基于自适应LASSO（Least Absolute Shrink age and Selection Operator）回归构建特征解耦层，通过动态调整惩罚项系数，筛选气象、拓扑和动态负荷等高维特征，生成低冗余特征子集。其次，利用XGBoost算法评估特征重要性，量化各变量对合环电流的边际贡献，增强模型可解释性。随后，构建GRU时序建模模块，借助门控机制捕捉长期依赖关系，并引入注意力机制对隐含状态动态加权，突出关键时间窗口的电流响应特征。为进一步量化预测不确定性，采用Bootstrap方法分析预测误差的统计分布，构建误差置信区间，并将其与点预测结果融合，形成覆盖不确定性的区间预测。最终输出具备概率信息的合环电流预测结果，为电网运行决策提供更可靠的参考依据。
