Access NLR (formerly NREL) ResStock and ComStock building stock datasets for load modeling

Steps

1. Navigate to resstock.nlr.gov/datasets (ResStock) and nlr.gov/buildings/comstock (ComStock) to browse available dataset releases; as of 2025, ResStock 2025.1 and ComStock 2025-2 are the latest releases — note the dataset release version for reproducibility.
2. Access the underlying data from AWS Open Data Registry: the datasets are hosted in the S3 bucket s3://oedi-data-lake/nrel-pds-building-stock/ under organized prefixes by dataset type and release year (the bucket path itself has not been renamed); use the AWS CLI or boto3 with anonymous access (no credentials required for this public bucket).
3. Use the BuildStockQuery Python library (available on PyPI and documented at NLR's GitHub, the NatLabRockies org — formerly hosted under the NREL org name) to query ResStock and ComStock results efficiently without downloading the full multi-terabyte dataset; BuildStockQuery supports filtering by geography, building type, vintage, upgrade scenario, and energy variables against Parquet files on S3.
4. Understand the dataset structure: each building is represented by a sampled 'dwelling unit' with characteristics from the ResStock or ComStock probability distributions; results include annual energy consumption by end use (HVAC, water heating, lighting, appliances) and optional timeseries load profiles at 15-minute or hourly resolution.
5. For load shape modeling, download the end-use load profile (EULP) timeseries files for the target climate zone and building type combination; files are in Parquet format and contain 8,760 hourly (or 35,040 fifteen-minute) rows per representative building.
6. Cite the dataset DOI and release version in any published analysis; NLR updates datasets annually and results can differ between releases due to model improvements — do not mix results from different release years.