Data

Tools

Indexes

Applications

Experiments

Open Source Science

sitinglab

This repository contains a collection of user-friendly tutorials and guides for working with the Siting Lab data within the context of the reV model. The python code examples demonstrate the creation and transformation of Siting Lab data into reV compliant format as well as working with the reV model inputs and outputs.

https://github.com/nrel/sitinglab

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.8%) to scientific vocabulary

Keywords

capacity cost-of-energy cost-of-transmission energy-analysis generation geothermal-energy renewable-energy siting siting-lab solar-energy supply-curve utility-scale wind-energy
Last synced: 6 months ago · JSON representation ·

Repository

This repository contains a collection of user-friendly tutorials and guides for working with the Siting Lab data within the context of the reV model. The python code examples demonstrate the creation and transformation of Siting Lab data into reV compliant format as well as working with the reV model inputs and outputs.

Basic Info
Statistics
  • Stars: 4
  • Watchers: 3
  • Forks: 5
  • Open Issues: 1
  • Releases: 1
Topics
capacity cost-of-energy cost-of-transmission energy-analysis generation geothermal-energy renewable-energy siting siting-lab solar-energy supply-curve utility-scale wind-energy
Created almost 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Welcome to Siting Lab Tutorials!

Release Documentation Static Badge Pixi Badge

This repository contains a collection of user-friendly tutorials and guides for working with the Siting Lab data within the context of the reV model. The python code examples demonstrate the creation and transformation of Siting Lab data into reV compliant format as well as working with the reV model inputs and outputs.

Getting Started

To interactively access the tutorial notebooks in this repository, first grab a copy of the repository by cloning it from GitHub:

$ git clone git@github.com:NREL/SitingLab.git

Before running any of the notebook tutorials, you should set up a Python environment that contains all the required dependencies and can launch jupyter for you.

Environment Setup

All instructions below assume you are executing the commands from the root directory of the Siting Lab Tutorial code repository you just downloaded.

Using Pixi (recommended)

We use pixi to manage cross-platform Siting Lab Tutorial environments. This tool allows developers to install libraries and dependencies in a compatible and reproducible way. We keep a version-controlled pixi.lock in the repository to allow locking with the full requirements tree so that behaviors and results can easily be reproduced.

To use pixi, simply install it using the link above and then run the following command in the root directory of the Siting Lab Tutorial code repository:

$ pixi run jupyter lab

Once the server starts, you can navigate to the URL shown on the terminal and access any notebook tutorial you wish!

Using Pip

You can install all the packages required for Siting Lab Tutorials using Python's native package installer pip. We strongly recommend using an environment manager like conda or mamba in this case. The steps below assume you have installed conda on your machine.

1) Create a conda env: conda create --name slt python=3.11.

2) Activate the newly-created conda env: conda activate slt.

3) Install Siting Lab Tutorial dependencies using pip: pip install .

Once your environment is installed and activated, you can run the following command to launch the jupyter server:

$ jupyter lab

Once the server starts, you can navigate to the URL shown on the terminal and access any notebook tutorial you wish!

Owner

  • Name: National Renewable Energy Laboratory
  • Login: NREL
  • Kind: organization
  • Location: Golden, CO

Citation (CITATION.cff) 

cff-version: 1.2.0
title: Siting Lab Tutorials
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - family-names: Pinchuk
    given-names: Pavlo
    affiliation: National Renewable Energy Laboratory
    orcid: 'https://orcid.org/0000-0003-4736-4728'
  - family-names: Lopez
    given-names: Anthony
    affiliation: National Renewable Energy Laboratory
    orcid: 'https://orcid.org/0000-0002-5006-1675'
  - family-names: Williams
    given-names: Travis
    affiliation: National Renewable Energy Laboratory
    orcid: 'https://orcid.org/0000-0003-0973-1865'
  - family-names: Igwe
    given-names: Victor
    affiliation: National Renewable Energy Laboratory
repository-code: 'https://github.com/NREL/SitingLab'
url: 'https://data.openei.org/siting_lab'
abstract: >-
  The Siting Lab Tutorials repository contains a collection
  of user-friendly tutorials and guides for working with the
  Siting Lab data within the context of the reV model. The
  python code examples demonstrate the creation and
  transformation of Siting Lab data into reV compliant
  format as well as working with the reV model inputs and
  outputs.
keywords:
  - Energy Analysis
  - reV
  - Siting Lab
  - Supply Curve
  - siting
  - utility-scale
  - capacity
  - generation
  - cost of energy
  - cost of transmission
  - renewable energy
  - solar energy
  - wind energy
license: BSD-3-Clause
version: 1.0.0
date-released: '2024-10-01'
references:
  - authors:
      - family-names: Lopez
        given-names: Anthony
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0000-0002-5006-1675"
      - family-names: Pinchuk
        given-names: Pavlo
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0000-0003-4736-4728"
      - family-names: Gleason
        given-names: Michael
        affiliation: National Renewable Energy Laboratory
      - family-names: Cole
        given-names: Wesley
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0000-0002-9194-065X"
      - family-names: Mai
        given-names: Trieu
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0000-0003-1751-3892"
      - family-names: Williams
        given-names: Travis
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0000-0003-0973-1865"
      - family-names: Roberts
        given-names: Owen
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0000-0002-8945-3967"
      - family-names: Rivers
        given-names: Marie
        affiliation: National Renewable Energy Laboratory
      - family-names: Bannister
        given-names: Mike
        affiliation: National Renewable Energy Laboratory
      - family-names: Thomson
        given-names: Sophie-Min
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0009-0005-9966-3887"
      - family-names: Zuckerman
        given-names: Gabe
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0000-0002-4500-2662"
      - family-names: Sergi
        given-names: Brian
        affiliation: National Renewable Energy Laboratory
        orcid: "https://orcid.org/0000-0002-3453-9878"
    title: "Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States (2023 Edition)"
    type: report
    doi: 10.2172/2283517
  - authors:
      - name: "Geospatial Data Science, NREL"
    title: "United States Utility-Scale PV Supply Curves 2023"
    type: data
    doi: 10.25984/2428989
  - authors:
      - name: "Geospatial Data Science, NREL"
    title: "United States Land-based Wind Supply Curves 2023"
    type: data
    doi: 10.25984/2428990
  - authors:
      - name: "Geospatial Data Science, NREL"
    title: "Airport and Heliport Setbacks"
    type: data
    doi: 10.25984/2441167
  - authors:
      - name: "Geospatial Data Science, NREL"
    title: "Next Generation Weather Radar (NEXRAD) Setback (4-km)"
    type: data
    doi: 10.25984/2441168
  - authors:
      - name: "Geospatial Data Science, NREL"
    title: "Wind Turbine Oil and Gas Pipeline Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter"
    type: data
    doi: 10.25984/2441187
  - authors:
      - name: "Geospatial Data Science, NREL"
    title: "Wind Turbine Structure Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter"
    type: data
    doi: 10.25984/2441180

GitHub Events

Total
  • Watch event: 3
  • Push event: 4
  • Fork event: 5
Last Year
  • Watch event: 3
  • Push event: 4
  • Fork event: 5