Work

Impedance-Informed and Self-Adaptive Graph Neural Networks for Short-Term Load Forecasting in Industrial Distribution Networks

Year: 2026

Type: article

Abstract: Graph neural networks are often applied to load forecasting on the assumption that electrically related meter points should be modeled jointly, but this is rarely tested against a graph-free control, and building the graph usually requires a network model that is not always available. We ask whether an impedance-informed graph—estimated directly from PT/CT voltage and current, with no topology model—improves short-term load forecasting beyond what the forecasting architecture achieves alone. We compare an impedance-informed ST-GCN against binary and correlation-based graphs, a self-adaptive learned graph, a graph-free (identity-adjacency) ST-GCN control, an LSTM baseline, and a persistence baseline, at two industrial facilities (six and nine buses), using chronological splits, five seeds, and Bonferroni-corrected significance testing. The result is conditional and architecture-dependent. At one site, LSTM significantly beat every ST-GCN variant, including the graph-free control, so the shortfall is architectural, not graph-specific. At the other, every ST-GCN variant beat LSTM at the conventional level (not after Bonferroni correction), and the impedance graph gave among the lowest RMSE (20.2% below an untuned LSTM, 13.1% below a tuned LSTM); however, the graph-free control alone captured most of this gain, and impedance’s further improvement over it was not significant. Lagged cross-correlation, Granger-causality, and input-perturbation analyses corroborate this: the trained ST-GCN’s sensitivity tracks the impedance graph more closely than that of LSTM does, and a self-adaptive graph converges toward the impedance graph’s structure, with seed-to-seed variability, only where graph structure helps. Graph-based load forecasting is therefore not universally beneficial; most of its benefit traces to the sequence-modeling architecture rather than to any specific graph, and both claims should be checked against a graph-free control and corrected significance tests before deployment.

Source: Energies

DOI: 10.3390/en19215255

Authors: Seunghyeon Park, Yongho Kim

Institution: Korea Electronics Technology Institute

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,137