Energy resource control with private purterbed timeseries data
- 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.
@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},
}