gsa_spatial_temporal
Science Score: 62.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: arxiv.org -
○Academic email domains
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✓Institutional organization owner
Organization kth-desa has institutional domain (www.energy.kth.se) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.5%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: KTH-dESA
- License: mit
- Language: Python
- Default Branch: main
- Size: 5.79 MB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 5
- Releases: 2
Metadata Files
README.md
Quantifying the relative importance of the spatial and temporal resolution in energy systems optimisation model
This repository is for the paper
Nandi Moksnes (1) *, William Usher (1) 1) KTH Royal Institute of Technology
To be able to run the model you need to have approx 256 GB RAM. The workflow is only tested on a Windows computer, therefore, there might be small adjustements needed for other OS.
Modelled input parameters
Python dependencies
The workflow has a number packages that needs to be installed.
The easiest way to install the Python packages is to use miniconda.
Obtain the miniconda package (https://docs.conda.io/en/latest/miniconda.html): 1) Add the conda-forge channel: conda config --add channels conda-forge 2) Create a new Python environment: conda env create -f environment.yml 3) Activate the new environment: conda activate GSA
R
To download the capacityfactors for solar and wind you need to have R on your computer. You can download R for free https://www.r-project.org/ You also need to install the package "curl" which you install through the R commander
install.packages("curl")Required accounts (free to register)
To run the code you need to create accounts in the following places: - https://www.renewables.ninja/ and get the token to download several files per hour - https://payneinstitute.mines.edu/eog/nighttime-lights/ and the password is entered in the first cell in the notebook
Run the model
Run first the src/buildinitialcountrydata.py and make sure that the base files look as expected. Then run the src/scenario_builder.py to build all the scenarios.
Owner
- Name: KTH division of Energy Systems
- Login: KTH-dESA
- Kind: organization
- Location: Sweden
- Website: https://www.energy.kth.se/research/energy-systems
- Repositories: 59
- Profile: https://github.com/KTH-dESA
Citation (CITATION.cff)
cff-version: 1.2.0
authors:
- family-names: "Moksnes"
given-names: "Nandi"
title: "GSA_Spatial_temporal"
version: 0.1.0
date-released: 2023-10-18
url: "https://github.com/KTH-dESA/GSA_Spatial_temporal"
preferred-citation:
message: "If you use this software, please cite it as below."
authors:
- family-names: "Moksnes"
given-names: "Nandi"
orcid: "https://orcid.org/0000-0002-8641-564X"
- family-names: "Usher"
given-names: "William"
orcid: "https://orcid.org/0000-0001-9367-1791"
doi: https://doi.org/10.48550/arXiv.2310.10518
date-published: 2023-10
publisher:
name: Arxiv
start: 101263
title: "Quantifying the relative importance of the spatial and temporal resolution in energy systems optimisation model"
type: pre-print
url: "https://arxiv.org/abs/2310.10518"
GitHub Events
Total
- Release event: 1
- Create event: 1
Last Year
- Release event: 1
- Create event: 1
Dependencies
- numpy >=1.21.6
- rasterstats >=0.8
- salib >=1.4
- sklearn >=0.0