sup3r

The Super Resolution for Renewable Resource Data software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.
https://github.com/NREL/sup3r

Category: Energy Systems
Sub Category: Renewable Energy Integration

Keywords

climate-change climate-data deep-learning generative-adversarial-network machine-learning renewable-energy solar-energy tensorflow wind-energy

Last synced: about 1 hour ago
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Repository metadata

The Super-Resolution for Renewable Resource Data (sup3r) software uses generative adversarial networks to create synthetic high-resolution wind and solar spatiotemporal data from coarse low-resolution inputs.

• Host: GitHub
• URL: https://github.com/NREL/sup3r
• Owner: NREL
• License: bsd-3-clause
• Created: 2021-10-28T19:05:05.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2025-04-17T20:29:35.000Z (13 days ago)
• Last Synced: 2025-04-19T15:17:06.621Z (11 days ago)
• Topics: climate-change, climate-data, deep-learning, generative-adversarial-network, machine-learning, renewable-energy, solar-energy, tensorflow, wind-energy
• Language: Python
• Homepage: https://nrel.github.io/sup3r/
• Size: 145 MB
• Stars: 96
• Watchers: 11
• Forks: 29
• Open Issues: 4
• Releases: 18
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE
  • Citation: CITATION.cff

README.rst

          Welcome to SUP3R!

|Docs| |Tests| |Linter| |PyPi| |PythonV| |Codecov| |Zenodo|

.. |Docs| image:: https://github.com/NREL/sup3r/workflows/Documentation/badge.svg
    :target: https://nrel.github.io/sup3r/

.. |Tests| image:: https://github.com/NREL/sup3r/workflows/Pytests/badge.svg
    :target: https://github.com/NREL/sup3r/actions?query=workflow%3A%22Pytests%22

.. |Linter| image:: https://github.com/NREL/sup3r/workflows/Lint%20Code%20Base/badge.svg
    :target: https://github.com/NREL/sup3r/actions?query=workflow%3A%22Lint+Code+Base%22

.. |PyPi| image:: https://img.shields.io/pypi/pyversions/NREL-sup3r.svg
    :target: https://pypi.org/project/NREL-sup3r/

.. |PythonV| image:: https://badge.fury.io/py/NREL-sup3r.svg
    :target: https://badge.fury.io/py/NREL-sup3r

.. |Codecov| image:: https://codecov.io/gh/nrel/sup3r/branch/main/graph/badge.svg
    :target: https://codecov.io/gh/nrel/sup3r

.. |Zenodo| image:: https://zenodo.org/badge/422324608.svg
    :target: https://zenodo.org/badge/latestdoi/422324608

.. inclusion-intro

The Super Resolution for Renewable Resource Data (sup3r) software uses
generative adversarial networks to create synthetic high-resolution wind and
solar spatiotemporal data from coarse low-resolution inputs. To get started,
check out the sup3r command line interface `(CLI)
`__.

Installing sup3r
================

NOTE: The installation instruction below assume that you have python installed
on your machine and are using `conda `__
as your package/environment manager.

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

1. Create a new environment: ``conda create --name sup3r python=3.11``

2. Activate environment: ``conda activate sup3r``

3. Install sup3r: ``pip install NREL-sup3r``

4. Run this if you want to train models on GPUs: ``pip install tensorflow[and-cuda]``

   4.1 For OSX use instead: ``python -m pip install tensorflow-metal``

Option 2: Clone repo (recommended for developers)
-------------------------------------------------

1. from home dir, ``git clone git@github.com:NREL/sup3r.git``

2. Create ``sup3r`` environment and install package
    1) Create a conda env with python: ``conda create --name sup3r python=3.11``
    2) Run the command: ``conda activate sup3r``
    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 ``sup3r`` and its dependencies by running:
       ``pip install .`` (or ``pip install -e .`` if running a dev branch
       or working on the source code)
    6) Run this if you want to train models on GPUs: ``pip install tensorflow[and-cuda]``
    7) *Optional*: Set up the pre-commit hooks with ``pip install pre-commit`` and ``pre-commit install``

Recommended Citation
====================

Update with current version and DOI:

Brandon Benton, Grant Buster, Guilherme Pimenta Castelao, Malik Hassanaly, Pavlo Pinchuk, Slater Podgorny, Andrew Glaws, and Ryan King. Super Resolution for Renewable Resource Data (sup3r). https://github.com/NREL/sup3r (version v0.2.0), 2024. DOI: 10.5281/zenodo.14042894

Acknowledgments
===============

This work 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. This research was supported by the Grid Modernization Initiative of the U.S. Department of Energy (DOE) as part of its Grid Modernization Laboratory Consortium, a strategic partnership between DOE and the national laboratories to bring together leading experts, technologies, and resources to collaborate on the goal of modernizing the nation’s grid. Funding provided by the the DOE Office of Energy Efficiency and Renewable Energy (EERE), the DOE Office of Electricity (OE), DOE Grid Deployment Office (GDO), the DOE Office of Fossil Energy and Carbon Management (FECM), and the DOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER), 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.

        

Citation (CITATION.cff)

cff-version: 1.2.0
title: The Super-Resolution for Renewable Resource Data (sup3r)
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - family-names: Benton
    given-names: Brandon
    affiliation: NREL
    orcid: "https://orcid.org/0009-0008-9931-2050"
  - family-names: Buster
    given-names: Grant
    affiliation: NREL
    orcid: "https://orcid.org/0000-0001-8616-8100"
  - family-names: Hassanaly
    given-names: Malik
    affiliation: NREL
    orcid: "https://orcid.org/0000-0002-0425-9090"
  - family-names: Pinchuk
    given-names: Pavlo
    affiliation: NREL
    orcid: "https://orcid.org/0000-0003-4736-4728"
  - family-names: Podgorny
    given-names: Slater
    affiliation: NREL
    orcid: "https://orcid.org/0009-0008-4903-411X"
  - family-names: Glaws
    given-names: Andrew
    affiliation: NREL
    orcid: "https://orcid.org/0000-0002-7268-1883"
  - family-names: King
    given-names: Ryan
    affiliation: NREL
    orcid: "https://orcid.org/0000-0002-0591-7139"
identifiers:
  - type: doi
    value: 10.5281/zenodo.6808547
repository-code: 'https://github.com/NREL/sup3r'
abstract: >-
  The Super Resolution for Renewable Resource Data (sup3r)
  software uses generative adversarial networks to create
  synthetic high-resolution wind and solar spatiotemporal
  data from coarse low-resolution inputs.
keywords:
  - machine-learning
  - deep-learning
  - generative-adversarial-network
  - climate-change
  - tensorflow
  - climate-data
  - solar-energy
  - renewable-energy
  - wind-energy
license: BSD-3-Clause
commit: 6d0f100
version: 0.1.2
date-released: '2023-12-18'

---

Owner metadata

• Name: National Renewable Energy Laboratory
• Login: NREL
• Email:
• Kind: organization
• Description:
• Website: http://www.nrel.gov
• Location: Golden, CO
• Twitter:
• Company:
• Icon url: https://avatars.githubusercontent.com/u/1906800?v=4
• Repositories: 599
• Last ynced at: 2024-12-18T09:41:07.898Z
• Profile URL: https://github.com/NREL

---

GitHub Events

Total

• Create event: 40
• Issues event: 3
• Release event: 5
• Watch event: 18
• Delete event: 34
• Issue comment event: 9
• Push event: 355
• Pull request review event: 71
• Pull request review comment event: 50
• Pull request event: 57
• Fork event: 20

Last Year

• Create event: 40
• Issues event: 3
• Release event: 5
• Watch event: 18
• Delete event: 34
• Issue comment event: 9
• Push event: 355
• Pull request review event: 71
• Pull request review comment event: 50
• Pull request event: 57
• Fork event: 20

---

Committers metadata

Last synced: 1 day ago

Name	Email	Commits
bnb32	b****n@n****v	1665
grantbuster	g****r@n****v	601
Gui Castelao	g****e@c****t	233
Malik	m****y@g****m	155
ppinchuk	p****k@n****v	46
Brandon Benton	b****b@c****u	17
Slater Podgorny	9****9	6
Slater Podgorny	s****n@k****v	1

Committer domains:

• nrel.gov: 3
• kl1.head.cm.kestrel.hpc.nrel.gov: 1
• cornell.edu: 1
• castelao.net: 1

---

Issue and Pull Request metadata

Last synced: 1 day ago

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/NREL/sup3r

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Package metadata

• Total packages: 1
• Total downloads:
  • pypi: 545 last-month
• Total dependent packages: 0
• Total dependent repositories: 0
• Total versions: 18
• Total maintainers: 2

---

Dependencies

Score: 12.989060530089908