/DER_ControlPrivateTimeSeries

Energy resource control

Primary LanguagePythonMIT LicenseMIT

Energy Resource Control via Privacy Preserving Data

Energy resource control with private purterbed timeseries data

Setup and Dependencies

  • Python 3.x/numpy/scipy/
  • cvxpy (We use v0.4.1, but should be able to run at v1.0 with minor tweaks)
  • PyTorch >= 0.4.1 [recommend version >=1.1.0]
  • pandas >= 23.0
  • matplotlib, seaborn (optional)

if using GPU, setup CUDA (optional).

This repo contains the experiments in the following paper "Energy Resource Control via Privacy Preserving Data". arxiv link

@article{chen2020energy,
  title={Energy resource control via privacy preserving data},
  author={Chen, Xiao and Navidi, Thomas and Rajagopal, Ram},
  journal={Electric Power Systems Research},
  volume={189},
  pages={106719},
  year={2020},
  publisher={Elsevier}
}

To test our parallel batched solver, simply run the

profiling_runtime.py

which is located under /CaseStudy_Synthetic/ folder.

check_priv

check_ctrl_gap

reference

@InProceedings{amos2017optnet,
  title = {{O}pt{N}et: Differentiable Optimization as a Layer in Neural Networks},
  author = {Brandon Amos and J. Zico Kolter},
  booktitle = {Proceedings of the 34th International Conference on Machine Learning},
  pages = {136--145},
  year = {2017},
  volume = {70},
  series = {Proceedings of Machine Learning Research},
  publisher ={PMLR},
}

@article{diffcp2019,
    author       = {Agrawal, A. and Barratt, S. and Boyd, S. and Busseti, E. and Moursi, W.},
    title        = {Differentiating through a Cone Program},
    journal      = {Journal of Applied and Numerical Optimization},
    year         = {2019},
    volume       = {1},
    number       = {2},
    pages        = {107--115},
}