https://github.com/nhchristianson/robust_ml_grid
Code accompanying the paper "Robust Machine-Learned Algorithms for Efficient Grid Operation"
https://github.com/nhchristianson/robust_ml_grid
Last synced: 2 months ago
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Code accompanying the paper "Robust Machine-Learned Algorithms for Efficient Grid Operation"
- Host: GitHub
- URL: https://github.com/nhchristianson/robust_ml_grid
- Owner: nhchristianson
- License: other
- Created: 2023-12-12T16:22:14.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-31T19:42:06.000Z (9 months ago)
- Last Synced: 2025-01-31T16:55:23.220Z (4 months ago)
- Language: Jupyter Notebook
- Size: 74 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Robust ML for Grid Operation
This repository contains code accompanying the paper "Robust Machine-Learned Algorithms for Efficient Grid Operation", including code for running experiments and generating figures. A part of this code - specifically, the cogeneration plant model, associated data, and utilities - is modified from the ``CogenEnv`` environment in the [SustainGym](https://chrisyeh96.github.io/sustaingym/) suite of reinforcement learning benchmarks.
## Folder structure
```
Experiments/
CALTECH_ONNX_Model/ # Cogeneration plant model
data/ # ambient condition data + experiment data storage
eds_experiments.ipynb # jupyter notebook with code for running experiments
figures/ # storage for figures generated by code
util.py # utilities for loading data
```
## License
This repository is released under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). See the [LICENSE](LICENSE) file for the full terms.