energyRt
Making Energy Systems Modeling as simple as a linear regression in R
Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (19.9%) to scientific vocabulary
Keywords
Repository
Making Energy Systems Modeling as simple as a linear regression in R
Basic Info
- Host: GitHub
- Owner: optimal2050
- License: agpl-3.0
- Language: R
- Default Branch: master
- Homepage: http://www.energyRt.org
- Size: 41.3 MB
Statistics
- Stars: 23
- Watchers: 5
- Forks: 8
- Open Issues: 0
- Releases: 12
Topics
Metadata Files
README.md
energyRt 
energyRt (energy system modeling R-toolbox /ˈɛnərdʒi ɑrt/) is a set of classes, methods, and functions that define a macro-language for energy system modeling within the R environment. This package offers a high-level, user-friendly interface that simplifies the development and analysis of complex energy models. By abstracting much of the underlying complexity, energyRt allows users to concentrate on strategic and analytical aspects rather than the technical details of coding.
Key Features:
- User-Friendly Interface: energyRt enables users to define energy systems, input data, and configure scenarios using intuitive, domain-specific commands. It is designed to be accessible for both experienced modelers and those new to the field.
- Seamless R Integration: The package integrates seamlessly with R’s extensive ecosystem of packages, allowing users to utilize powerful data handling and visualization tools within their energy modeling projects.
- The energyRt optimization model is implemented in four widely-used mathematical programming languages, both proprietary and open-source: GAMS, GLPK/Mathprog, Python/Pyomo, Julia/JuMP. The package is designed to work seamlessly with any of these versions, allowing users to solve models using their preferred software while ensuring consistent and equivalent results across all platforms.
- Modular Model Construction: energyRt supports the construction of models in a modular fashion, enabling incremental development, individual component testing, and code reuse across different projects. This modularity, combined with R’s interactive environment, promotes an iterative approach to modeling where assumptions can be tested, and results explored in real-time.
- Applications: energyRt is designed to facilitate the creation of sophisticated energy system models, offering both flexibility and depth for detailed analysis. It is an essential tool for researchers, policymakers, and industry professionals engaged in long-term energy system planning, energy transition, and decarbonization efforts.
The package website: https://energyrt.org\ Documentation in progress: https://energyrt.github.io/book/
Development status
energyRt is currently in preparation for its first release and publication on CRAN. The major milestone for the package is the version v0.50 ("half-way-there"), a proof of concept with a full-featured and efficient model written in four math-prog languages, with R-interface for the model design, processing results, and producing reports. This version will have frozen model code, classes and methods. Any updates will address only potential fixes and new features with minimal impact on already existing modeling projects.
Further development, versions starting from v0.9 towards the v1.0 will have fully reviewed model and classes with the goal to further increase efficiency, reduce memory footprint and computational burden for both the model and its R interface, and significantly extend features.
Installation
Assuming that R is already installed (if not, please download and
install from https://www.r-project.org/), we also recommend RStudio
(https://www.rstudio.com/), a powerful IDE (Integrated Development
Environment) for R. The installation of the package is done via the
pak or remotes packages:
pak::pkg_install("energyRt/energyRt@v0.50")\
or\
remotes::install_github("energyRt/energyRt", ref = "v0.50")
The next step would be to install at least one of the solvers: GAMS, GLPK, Python/Pyomo, Julia/JuMP. Please refer to the respective websites for installation instructions. More detaileds is available on the IDEEA model website, a project based on the energyRt package.
Owner
- Login: optimal2050
- Kind: user
- Repositories: 3
- Profile: https://github.com/optimal2050
GitHub Events
Total
- Watch event: 1
- Push event: 1
Last Year
- Watch event: 1
- Push event: 1
Committers
Last synced: 6 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| vpotashnikov | p****u@g****m | 969 |
| olugovoy | o****y@g****m | 315 |
| “olugovoy” | “****y@g****” | 22 |
| ideea-model | o****y@e****g | 8 |
| energyRt | 5****t | 7 |
| Michaja Pehl | p****l@p****e | 1 |
| VZhikhareva | v****a@g****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 8
- Total pull requests: 40
- Average time to close issues: about 1 year
- Average time to close pull requests: 7 days
- Total issue authors: 6
- Total pull request authors: 4
- Average comments per issue: 2.0
- Average comments per pull request: 0.2
- Merged pull requests: 38
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 7
- Average time to close issues: N/A
- Average time to close pull requests: 1 minute
- Issue authors: 0
- Pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- olugovoy (2)
- AboodaA (2)
- charliemsl (1)
- awanyulianto (1)
- BjoernLaemmerzahl (1)
- infsum (1)
Pull Request Authors
- olugovoy (29)
- optimal2050 (5)
- vpotashnikov (5)
- michaja (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- R >= 3.6 depends
- parallel * depends
- DBI * imports
- RSQLite * imports
- data.table * imports
- rpivotTable * imports
- tidyverse * imports
- knitr * suggests
- rmarkdown * suggests
- sp * suggests