Research Article
Big Data-Driven Short-Term Load Forecasting in Smart Grids: A Spatio-Temporal Dynamic Graph Transformer with Uncertainty-Aware Attention
@ARTICLE{10.4108/ew.13839, author={Jing Zhou and Jiyue Long and Xiaohong Long}, title={Big Data-Driven Short-Term Load Forecasting in Smart Grids: A Spatio-Temporal Dynamic Graph Transformer with Uncertainty-Aware Attention}, journal={EAI Endorsed Transactions on Energy Web}, volume={13}, number={1}, publisher={EAI}, journal_a={EW}, year={2026}, month={8}, keywords={short-term load forecasting, smart grid, dynamic graph neural network, Transformer, uncertainty quantification, deep learning, spatio-temporal data mining}, doi={10.4108/ew.13839} }- Jing Zhou
Jiyue Long
Xiaohong Long
Year: 2026
Big Data-Driven Short-Term Load Forecasting in Smart Grids: A Spatio-Temporal Dynamic Graph Transformer with Uncertainty-Aware Attention
EW
EAI
DOI: 10.4108/ew.13839
Abstract
Short-term load forecasting for modern smart grids must jointly model long-range temporal patterns, correlations across many metering points, and the uncertainty required for operational decision-making. We propose ST-DGT-UA, a spatio-temporal dynamic graph Transformer that (i) embeds heterogeneous covariates with convolutional projections and Time2Vec, (ii) learns a time-varying adjacency matrix from node representations, (iii) couples spatial graph attention with temporal ProbSparse attention for efficient long-sequence modeling, and (iv) outputs multiple conditional quantiles trained with multi-quantile Pinball loss. Experiments on GEFCom2014 and the UCI Electricity Load Diagrams datasets show that ST-DGT-UA achieves MAPE/RMSE of 1.92/145.6 on GEFCom2014 and 2.65/42.3 on UCI, and improves probabilistic quality with Pinball Loss 0.024 and CRPS 0.043. These results indicate that learning dynamic, data-driven spatial dependencies is beneficial for multi-node load forecasting where correlations evolve over time.
Copyright © 2026 Xiaohong Long et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (CC BY-NC-SA 4.0), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
