Datasets:
File size: 3,943 Bytes
fa60d04 36686bc fa60d04 7893974 fa60d04 36686bc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 |
---
license: cc-by-sa-4.0
tags:
- energy
- optimization
- optimal_power_flow
- power_grid
pretty_name: PGLearn Optimal Power Flow (small)
size_categories:
- 1M<n<10M
task_categories:
- tabular-regression
viewer: false
---
# PGLearn optimal power flow (small) dataset
This dataset contains input data and solutions for small-size Optimal Power Flow (OPF) problems.
Original case files are based on instances from Power Grid Lib -- Optimal Power Flow ([PGLib OPF](https://github.com/power-grid-lib/pglib-opf));
this dataset comprises instances corresponding to systems with up to 300 buses.
## Download instructions
The recommended way to download this dataset is through the [HuggingFace client library](https://huggingface.co/docs/hub/datasets-downloading#using-the-hugging-face-client-library).
### Downloading the entire dataset
1. Install `huggingface_hub` (see official [installation instructions](https://huggingface.co/docs/huggingface_hub/installation))
```bash
pip install --upgrade huggingface_hub
```
2. Download the dataset.
It is recommended to save files to a local directory
```py
from huggingface_hub import snapshot_download
REPO_ID = "PGLearn/PGLearn-Small"
LOCAL_DIR = "<path/to/local/directory>"
snapshot_download(repo_id=REPO_ID, repo_type="dataset", local_dir=LOCAL_DIR)
```
Note that by default, `snapshot_download` saves files to a local cache.
3. De-compress all the files
```bash
cd <path/to/local/directory>
find ./ -type f -name "*.gz" -exec unpigz -v {} +
```
### Downloading individual files
The entire PGLearn-Small collection takes about 180GB of disk space (compressed).
To avoid large disk usage and long download times, it is possible to download only a subset of the files.
This approach is recommended for users who only require a subset of the dataset, for instance:
* a subset of cases
* a specific OPF formulation (e.g. only ACOPF)
* only primal solutions (as opposed to primal and dual)
This can be achieved by using the `allow_patterns` and `ignore_patterns` parameters (see [official documentation](https://huggingface.co/docs/huggingface_hub/guides/download#filter-files-to-download)),
in lieu of step 2. above.
* To download only the `14_ieee` and `30_ieee` cases:
```py
REPO_ID = "PGLearn/PGLearn-Small"
CASES = ["14_ieee", "30_ieee"]
LOCAL_DIR = "<path/to/local/dir>"
snapshot_download(repo_id=REPO_ID, allow_patterns=[f"{case}/" for case in CASES], repo_type="dataset", local_dir=LOCAL_DIR)
```
* To download a specific OPF formulation
(the repository structure makes it simpler to exclude non-desired OPF formulations)
```py
REPO_ID = "PGLearn/PGLearn-Small"
ALL_OPFS = ["ACOPF", "DCOPF", "SOCOPF"]
SELECTED_OPFS = ["ACOPF", "DCOPF"]
LOCAL_DIR = "<path/to/local/dir>"
snapshot_download(repo_id=REPO_ID, ignore_patterns=[f"*/{opf}/*" for opf in ALL_OPFS if opf not in SELECTED_OPFS], repo_type="dataset", local_dir=LOCAL_DIR)
```
* To download only primal solutions
```py
REPO_ID = "PGLearn/PGLearn-Small"
LOCAL_DIR = "<path/to/local/dir>"
snapshot_download(repo_id=REPO_ID, ignore_patterns="*dual.h5.gz", repo_type="dataset", local_dir=LOCAL_DIR)
```
## Contents
For each system (e.g., `14_ieee`, `118_ieee`), the dataset provides multiple OPF instances,
and corresponding primal and dual solutions for the following OPF formulations
* AC-OPF (nonlinear, non-convex)
* DC-OPF approximation (linear, convex)
* Second-Order Cone (SOC) relaxation of AC-OPF (nonlinear, convex)
This dataset was created using [OPFGenerator](https://github.com/AI4OPT/OPFGenerator);
please see the [OPFGenerator documentation](https://ai4opt.github.io/OPFGenerator/dev/) for details on mathematical formulations.
## Use cases
The primary intended use case of this dataset is to learn a mapping from input data to primal and/or dual solutions. |