ACM e-Energy · 2026
Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning
ACM International Conference on Future and Sustainable Energy Systems
Can appliances be identified when solar and storage obscure meter readings?
DualNILM jointly recognizes appliance states and identifies energy injected behind the meter. Its transformer architecture combines temporal learning tasks to separate consumption from injections, with evaluation on measured and synthesized datasets.
Research themes
Connections
Recovering structure from limited measurements
Graph recovery estimates electrical network parameters. Charging curve clustering and energy disaggregation recover useful structure from observed behavior and aggregate meter readings.
Cite this paper
Xudong Wang, Guoming Tang, Junyu Xue, Srinivasan Keshav, Tongxin Li, Chris Ding. Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning. ACM International Conference on Future and Sustainable Energy Systems, 2026. https://doi.org/10.1145/3744255.3798113
BibTeX
@inproceedings{tongxin-dualnilm,
title = {{Energy Injection Identification enabled Disaggregation with Deep Multi-Task Learning}},
author = {Xudong Wang and Guoming Tang and Junyu Xue and Srinivasan Keshav and Tongxin Li and Chris Ding},
year = {2026},
booktitle = {ACM International Conference on Future and Sustainable Energy Systems},
url = {https://doi.org/10.1145/3744255.3798113},
doi = {10.1145/3744255.3798113}
}
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