| 张长开,王坤,李志宇,李宏超,戚晓芳.基于时空图神经网络的地铁供电系统负荷预测[J].电力需求侧管理,2026,28(2):64-69 |
| 基于时空图神经网络的地铁供电系统负荷预测 |
| Load forecasting of subway power supply system based on spatio-temporal graph neural networks |
| 投稿时间：2025-10-10 修订日期：2025-12-26 |
| DOI：10.3969/j.issn.1009-1831.2026.02.010 |
| 中文关键词: 图神经网络 地铁负荷预测 时序预测 动态学习图 |
| 英文关键词: graph neural networks subway load forecasting time-series prediction dynamic learning graph |
| 基金项目:中国城市轨道交通协会城轨装备核心技术攻关项目(2022ZBGG002) |
|
| 摘要点击次数: 0 |
| 全文下载次数: 0 |
| 中文摘要: |
| 地铁负荷预测可辅助地铁电力系统稳定和高效运行。现有的地铁电力负荷预测大多采用统计学或机器学习模型(如线性回归、支持向量机)等方法,难以有效捕捉地铁供电系统负荷的时空特性,特别是负荷的时变性和非线性等复杂特点,预测精度仍有待提高。为进一步提高地铁负荷预测精度,提出一种基于时空图神经网络的地铁供电负荷预测方法(spatial-temporal graph neural networks,STGNN),预测地铁运行时各个站点的电力负荷。STGNN从多个角度提取地铁各个站点间的时空关系,通过构建地理距离图、负荷相似性图及动态学习图等多视角时空图,全面捕捉地铁供电系统负荷的时空动态变化。其中动态学习图机制可自适应地调整邻接矩阵,增强预测模型对非线性及时间演变特征的感知能力。采用某市地铁线站点的电力负荷历史数据进行实验,结果表明,STGNN电力负荷预测精度达到89.37%,较XGBoost、LightGBM、LSTM和MTGNN模型分别提高3.16%、3.90%、11.38%和2.10%,验证了STGNN在地铁电力负荷预测具有广泛的应用前景。 |
| 英文摘要: |
| Subway load forecasting can facilitate the stable and efficient operation of subway power systems. Most existing methods for forecasting subway power load utilize statistical or machine learning models, such as linear regression or support vector machines. However, due to the difficulty in effectively capturing the spatial-temporal characteristics of subway power systems, particularly time-varying nature and non-linear complexities of the load, these methods are limited in the precision of prediction. To further enhance the precision of subway load forecasting, a subway power load forecasting method based on spatial-temporal graph neural networks (STGNN) is proposed to predict the power traction load of each station during subway operations. STGNN extracts spatial-temporal relationships from multiple perspectives of subway stations by constructing multiple-perspective spatial-temporal graphs that integrate a geographical distance graph, a load similarity graph, and a dynamic learning graph. It comprehensively captures the spatial-temporal dynamic changes of the subway power system, where the dynamic learning graph mechanism adaptively adjusts the adjacency matrix, thereby improving the ability of the model to perceive non-linearity and the evolutionary temporal characteristics. Experiments are conducted on historical data of power load fromsubway stationsin some city. Results show that STGNN achieves a high prediction precision of 89.37%, which is 3.16%, 3.90%, 11.38% and 2.10% higher than those of XGBoost, LightGBM, LSTM and MTGNN models respectively, indicating that STGNN has broad application prospects in subway power load forecasting. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|