基于时空图神经网络的地铁供电系统负荷预测OA

Subway load forecasting can facilitate the stable and efficient operation of subway power systems.Most existing methods for fore-casting 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 similari-ty 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 per-ceive 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.