An Edge-Deployable Lightweight Dynamic Spatio-Temporal Graph Neural Network for Joint Distribution-Network Line-Loss Prediction and Operating-State Recognition

Abstract

A lightweight dynamic spatio-temporal graph neural network, EdgeLite-DSTGNN, is proposed to address the intensified spatio-temporal coupling of line-loss rates, complex state correlations, and limited edge-terminal resources in distribution networks with high renewable-energy penetration. The method treats branches as graph nodes and constructs a dynamic sparse graph by integrating physical topology, time-varying electrical relationships, and adaptive associations. Shared features are extracted using depthwise separable dilated causal convolutions, Top-k sparse graph attention, and gated fusion to jointly perform multi-step line-loss-rate prediction and identify high line loss, overload, voltage violations, and reverse power flow. Knowledge distillation, structured pruning, INT8 quantization, and event-triggered graph construction are further incorporated for edge deployment. Experiments on modified IEEE 33- and 69-bus systems show that, compared with PC-GAT, the proposed method reduces MAE, RMSE, and MAPE by 13.76%, 12.80%, and 13.58%, respectively, while improving Macro-F1 by 1.7 percentage points. The final INT8 model reduces file size, peak memory, inference latency, and energy consumption by 93.8%, 73.0%, 81.8%, and 81.7%, respectively, and the event-triggered mechanism lowers the average end-to-end latency from 12.8 ms to 9.3 ms.