.png){kind=icon}
Return
Missing data-aware robust electrical load forecasting based on hierarchical downsampling-upsampling spatiotemporal graph network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apenergy.2025.127209.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Propose HDU-STGNN, an end-to-end load forecaster that is robust to missing data. • Introduce mask-aware temporal downsampling and hierarchical spatial coarsen–refine. • Achieve up to 17.4 % error reduction under 90 % missing rates on real load data.
Journal
IF:
11
Papers:
2.6W
Citations:
17.8W
Organization
Cited Papers
.png){kind=icon}
Ultra-Short-Term Forecasting of Large Distributed Solar PV Fleets Using Sparse Smart Inverter Data
PREAI
ShareSaveGinAR+: A Robust End-to-End Framework for Multivariate Time Series Forecasting With Missing Values
PREAI
ShareSaveShareSaveShareSaveShareSave
View more