Copper
Copper: a performance curve generator for building energy simulation - Published in JOSS (2023)
Science Score: 98.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 4 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org, zenodo.org -
✓Committers with academic emails
3 of 9 committers (33.3%) from academic institutions -
✓Institutional organization owner
Organization pnnl has institutional domain (www.pnnl.gov) -
✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Scientific Fields
Repository
Performance curve generator for building energy simulation
Basic Info
- Host: GitHub
- Owner: pnnl
- License: bsd-2-clause
- Language: Python
- Default Branch: develop
- Homepage: https://pnnl.github.io/copper/
- Size: 181 MB
Statistics
- Stars: 10
- Watchers: 4
- Forks: 5
- Open Issues: 8
- Releases: 11
Topics
Metadata Files
README.md
Copper
Copper is a building energy simulation performance curve generator for heating, ventilation, and air-conditioning equipment. It uses a genetic algorithm to modify existing or a set of aggregated existing performance curves to match specific design characteristics including full and part load energy performance metric values.
Copper generates performance curves that can be used in most common building energy software such as EnergyPlus or DOE-2.
Purpose
Results from building energy simulations largely depend on how heating and cooling equipment are modeled. As they rarely operate at their full load and rated performance it is important that reasonable data is used to model an heating or cooling equipment's part load performance. Copper was created to allow building energy modelers, engineers, and researchers to generate simulation-ready performance curves of heating and cooling equipment that not only capture their typical behavior at part load but are also generated to match a set of design characteristics such as full load and part load efficiencies.
While chillers, which are rated at both full load and part load through an integrated part load value (IPLV), are currently the main application of Copper, other heating and cooling equipment will be handled in the near future.
Web Application
https://copper.pnnl.gov/
Documentation
The documentation for Copper is hosted here, it includes installation instructions, quick tutorials, examples, API documentation, and more.
Contributing
Contributions (issues and pull requests) are welcomed and greatly appreciated. Guidelines are provided in the documentation.
Help
If you need help using Copper, please open an issue and one of the developers will get back to you at their earliest convenience.
Owner
- Name: Pacific Northwest National Laboratory (Public)
- Login: pnnl
- Kind: organization
- Email: dev-central@pnnl.gov
- Location: United States of America
- Website: http://www.pnnl.gov/
- Repositories: 351
- Profile: https://github.com/pnnl
This Org is intended for the hosting of approved released PNNL software repositories for public use and collaboration.
JOSS Publication
Copper: a performance curve generator for building energy simulation
Authors
Pacific Northwest National Laboratory, Richland, WA, USA
Pacific Northwest National Laboratory, Richland, WA, USA
Pacific Northwest National Laboratory, Richland, WA, USA
Pacific Northwest National Laboratory, Richland, WA, USA
Editor
Chris Vernon
Tags
python energy building simulation hvacGitHub Events
Total
- Create event: 15
- Release event: 3
- Issues event: 7
- Watch event: 1
- Delete event: 1
- Issue comment event: 15
- Push event: 34
- Pull request review event: 16
- Pull request review comment event: 21
- Pull request event: 25
- Fork event: 2
Last Year
- Create event: 15
- Release event: 3
- Issues event: 7
- Watch event: 1
- Delete event: 1
- Issue comment event: 15
- Push event: 34
- Pull request review event: 16
- Pull request review comment event: 21
- Pull request event: 25
- Fork event: 2
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Lerond, Jeremy | j****d@p****v | 248 |
| Rahman, Aowabin | a****n@p****v | 41 |
| Wan | h****n@p****v | 34 |
| Hanlong Wan | 6****0 | 3 |
| Aowabin Rahman | r****2@c****v | 2 |
| Aowabin Rahman | r****2@c****v | 2 |
| manansingh5 | m****5@g****m | 1 |
| ChristensenCode | 4****e | 1 |
| Jeremy Lerond | l****3@c****v | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 42
- Total pull requests: 99
- Average time to close issues: 5 months
- Average time to close pull requests: 14 days
- Total issue authors: 6
- Total pull request authors: 5
- Average comments per issue: 0.76
- Average comments per pull request: 0.59
- Merged pull requests: 84
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 1
- Pull requests: 33
- Average time to close issues: about 1 hour
- Average time to close pull requests: 19 days
- Issue authors: 1
- Pull request authors: 3
- Average comments per issue: 0.0
- Average comments per pull request: 0.76
- Merged pull requests: 24
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- lymereJ (23)
- wanhanlong1130 (7)
- samuelduchesne (6)
- jugonzal07 (4)
- mcornach (1)
- FWuellhorst (1)
Pull Request Authors
- lymereJ (94)
- aowabinr (12)
- wanhanlong1130 (10)
- manansingh5 (6)
- ChristensenCode (3)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- CoolProp *
- click *
- matplotlib *
- numpy *
- pandas *
- scipy *
- statsmodels *