Applied Energy · 2026
Counterfactual load forecasting with LLM-structured events and representation learning
Applied Energy
How might electricity demand change under a different news event?
NACF turns news into structured treatments and estimates load trajectories under alternative event conditions. Reweighting and representation balancing address observed confounding. Experiments examine factual accuracy and interpretable demand perturbations without claiming that unobserved counterfactual outcomes can be directly validated.
Research themes
Connections
Context becomes an energy system input
OpenCEM motivates shared contextual energy benchmarks. INSTRUCT MPC uses instructions for control, while counterfactual load forecasting represents news events as interventions on electricity demand.
Cite this paper
Yujie Chen, Yifei Gao, Runyao Yu, Yuhe Wu, Guangyu Wang, Yue Chen, Tongxin Li. Counterfactual load forecasting with LLM-structured events and representation learning. Applied Energy, 2026. https://doi.org/10.1016/j.apenergy.2026.128554
BibTeX
@article{tongxin-counterfactual-load,
title = {{Counterfactual load forecasting with LLM-structured events and representation learning}},
author = {Yujie Chen and Yifei Gao and Runyao Yu and Yuhe Wu and Guangyu Wang and Yue Chen and Tongxin Li},
year = {2026},
journal = {Applied Energy},
url = {https://doi.org/10.1016/j.apenergy.2026.128554},
doi = {10.1016/j.apenergy.2026.128554}
}
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