ACM e-Energy demo · 2025

Open In-Context Energy Management Platform

Yikai Lu, Tinko Sebastian Bartels, Ruixiang Wu, Fanzeng Xia, Xudong Wang, Yifei Wu, Haoxiang Yang, Tongxin Li

ACM International Conference on Future and Sustainable Energy Systems

What would a shared benchmark for contextual energy management provide?

OpenCEM presents a platform design connecting energy time series, events, human context, and simulation. An on campus solar and battery installation motivates evaluation of context sensitive control. The paper describes planned data and API capabilities.

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.

Open questions

Coordinated low carbon infrastructure

Can distributed energy and water resources adapt together under changing conditions?

Explore open directions

Cite this paper

Yikai Lu, Tinko Sebastian Bartels, Ruixiang Wu, Fanzeng Xia, Xudong Wang, Yifei Wu, Haoxiang Yang, Tongxin Li. Open In-Context Energy Management Platform. ACM International Conference on Future and Sustainable Energy Systems, 2025. https://doi.org/10.1145/3679240.3734678

BibTeX
@inproceedings{tongxin-opencem,
  title = {{Open In-Context Energy Management Platform}},
  author = {Yikai Lu and Tinko Sebastian Bartels and Ruixiang Wu and Fanzeng Xia and Xudong Wang and Yifei Wu and Haoxiang Yang and Tongxin Li},
  year = {2025},
  booktitle = {ACM International Conference on Future and Sustainable Energy Systems},
  url = {https://doi.org/10.1145/3679240.3734678},
  doi = {10.1145/3679240.3734678}
}
2026Applied Energy

Counterfactual load forecasting with LLM-structured events and representation learning

Yujie Chen, Yifei Gao, Runyao Yu, Yuhe Wu, Guangyu Wang, Yue Chen, Tongxin Li

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.