| --- |
| pretty_name: ArchEGraph-demo |
| license: apache-2.0 |
| task_categories: |
| - graph-ml |
| - time-series-forecasting |
| language: |
| - en |
| tags: |
| - building-energy |
| - simulation |
| - graph |
| - weather |
| - demo |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: manifest |
| default: true |
| data_files: |
| - split: train |
| path: manifest.csv |
| - config_name: split_demo |
| data_files: |
| - split: train |
| path: split/split_demo.csv |
| - config_name: split_demo_mesh |
| data_files: |
| - split: train |
| path: split/split_demo_mesh.csv |
| --- |
| |
| # ArchEGraph-demo |
|
|
| ArchEGraph-demo is a compact demo package of the ArchEGraph building-energy dataset for graph-based and weather-conditioned learning. |
|
|
| ## Dataset Summary |
|
|
| - Total cases in `manifest.csv`: 300 |
| - Unique buildings: 75 |
| - Unique weather IDs: 48 |
| - `n_steps`: always 8,760 |
| - `n_spaces` range: 2 to 132 |
|
|
| This package currently stores: |
|
|
| - `manifest.csv` (index of all demo cases) |
| - `building/` (75 files) |
| - `geometry/` (75 files) |
| - `weather/` (48 files) |
| - `energy/` (300 files) |
| - `split/` (demo split CSV files) |
|
|
| ## Data Layout |
|
|
| Each row in `manifest.csv` contains: |
|
|
| - `sample_id`: case ID (`building__weather` style) |
| - `source_job_tag`: source identifier |
| - `weather_id`: weather/location key |
| - `building_id`: building key |
| - `energy_file`: relative path to energy npz file under `energy/` |
| - `n_steps`: number of time steps |
| - `n_spaces`: number of spaces/zones |
|
|
| ## Included Split Files |
|
|
| - `split/split_demo.csv` (300 rows) |
| - `split/split_demo_mesh.csv` (300 rows) |
|
|
| `split/split_demo.csv` uses these columns: |
|
|
| - `case_id`, `sample_id`, `building_id`, `weather_id`, `subset`, `split`, `scenario` |
|
|
| `split/split_demo_mesh.csv` uses these columns: |
|
|
| - `building_id`, `split` |
|
|
| ## Quick Start |
|
|
| ```python |
| import pandas as pd |
| from pathlib import Path |
| |
| root = Path(".") # dataset root |
| manifest = pd.read_csv(root / "manifest.csv") |
| |
| row = manifest.iloc[0] |
| energy_path = root / "energy" / row["energy_file"] |
| building_path = root / "building" / f"{row['building_id']}.npz" |
| weather_path = root / "weather" / f"{row['weather_id']}.npz" |
| |
| print(row["sample_id"]) |
| print(energy_path, building_path, weather_path) |
| ``` |
|
|
| ## Notes |
|
|
| - This repository is the demo package, not the full PACK release. |
| - Energy files in this demo package are referenced by `energy_file` from `manifest.csv`. |
| - `split/split_demo.csv` and `split/split_demo_mesh.csv` provide ready-to-use predefined splits for the packaged demo samples. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite your project/paper and this Hugging Face dataset page. |
