Welcome to Sup3r!

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) here.

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=3.9

2. Activate environment: conda activate sup3r

3. Install sup3r: pip install NREL-sup3r

4. Run this if you want to train models on GPUs: conda install -c anaconda tensorflow-gpu

   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: conda create -n sup3r

   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: conda install -c anaconda tensorflow-gpu On Eagle HPC, you will need to also run pip install protobuf==3.20.* and pip install chardet

   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, Andrew Glaws, Ryan King. Super Resolution for Renewable Resource Data (sup3r). https://github.com/NREL/sup3r (version v0.0.3), 2022. DOI: 10.5281/zenodo.6808547

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. 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.