ArchEGraph/ArchEGraph-demo

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

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.