Welcome to the National Solar Radiation Data Base (NSRDB)!

The National Solar Radiation Database (NSRDB) software includes all the methods for the irradiance data processing pipeline. To get started, check out the NSRDB command line interface (CLI). Refer to the NREL website and the original journal article for more information on the NSRDB. For details on NSRDB variable units, datatypes, interpolation methods, and other attributes, see the NSRDB variable meta data and NSRDB variable descriptions.

The PXS All-Sky Irradiance Model

The PXS All-Sky Irradiance Model is the main physics package that calculates surface irradiance variables.

The NSRDB Data Model

The NSRDB Data Model is the data aggregation framework that sources, processes, and prepares data for input to All-Sky.

The MLClouds Model

The MLClouds Model is used to predict missing cloud properties (a.k.a. Gap Fill). The NSRDB interface with MLClouds can be found here.

Installation

Option 1: Install from PIP (recommended for analysts):

1. Create a new environment: conda create --name nsrdb python=3.9

2. Activate environment: conda activate nsrdb

3. Install nsrdb: pip install NREL-nsrdb

Option 2: Clone repo (recommended for developers)

1. from home dir, git clone git@github.com:NREL/nsrdb.git

2. Create nsrdb environment and install package

   1. Create a conda env: conda create -n nsrdb

   2. Run the command: conda activate nsrdb

   3. cd into the repo cloned in 1.

   4. Prior to running pip below, make sure the branch is correct (install from main!)

   5. Install nsrdb and its dependencies by running: pip install . (or pip install -e . if running a dev branch or working on the source code)

   6. Optional: Set up the pre-commit hooks with pip install pre-commit and pre-commit install

NSRDB Versions

Recommended Citation

Update with current version and DOI:

Grant Buster, Brandon Benton, Mike Bannister, Yu Xie, Aron Habte, Galen Maclaurin, Manajit Sengupta. National Solar Radiation Database (NSRDB). https://github.com/NREL/nsrdb (version v4.0.0), 2023. DOI: 10.5281/zenodo.10471523

Acknowledgments

This work (SWR-23-77) was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the DOE Grid Deployment Office (GDO), the DOE Advanced Scientific Computing Research (ASCR) program, the DOE Solar Energy Technologies Office (SETO), the DOE Wind Energy Technologies Office (WETO), the United States Agency for International Development (USAID), and the Laboratory Directed Research and Development (LDRD) program at the National Renewable Energy Laboratory. The research was performed using computational resources sponsored by the Department of Energy’s Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

*Note: The “Data Years” column shows which years of NSRDB data were updated at the time of version release. However, each NSRDB file should be checked for the version attribute, which should be a more accurate record of the actual data version.