digital_wra_data_standard
This standard data model and associated tools are intended as universal building blocks for Wind Energy Resource Assessment applications.
https://github.com/IEA-Task-43/digital_wra_data_standard
Category: Renewable Energy
Sub Category: Wind Energy
Keywords from Contributors
transforms archiving routes measur observation conversion compose projection bird annotation
Last synced: about 19 hours ago
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Repository metadata
IEA Task 43: pre-construction energy estimate data standard repository
- Host: GitHub
- URL: https://github.com/IEA-Task-43/digital_wra_data_standard
- Owner: IEA-Task-43
- License: bsd-3-clause
- Created: 2019-12-03T21:26:02.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2025-04-17T16:30:11.000Z (11 days ago)
- Last Synced: 2025-04-17T22:44:26.953Z (10 days ago)
- Language: Jupyter Notebook
- Size: 4.46 MB
- Stars: 60
- Watchers: 14
- Forks: 16
- Open Issues: 40
- Releases: 6
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: contributing.md
- License: license.txt
README.md
Digital WRA Data Standards
This repository is for the work, carried out by IEA Wind Task 43, on standardizing digital tools in wind resource
assessment (WRA). It currently consists of data models for wind resource measurements (WRA Data Model) and
digital calibration certificates. Along with associated tools, these are intended as universal building blocks for
wind energy yield assessment applications.
Mission
"Our mission is to make the energy yield assessment process more efficient, transparent and reproducible
through digitizing and automation."
What is a "Data Model"?
A data model is an abstract model of real-world entities that organizes elements of data and standardizes how they
relate to one another. In this instance, the WRA Data Model standardizes how properties of a wind resource measurement station (e.g.
latitude, longitude, anemometer serial number, installation height, logger slope, logger offset, etc.) are recorded and
how all the properties relate to each other. An implementation of the data model therefore describes how a specific met mast was
installed, how the sensors were mounted on that met mast, how these sensors were programmed into the logger and how
all these properties may have changed over time.
For more information on the definition of a Data Model and other terminology such as Schema, please see the
Task 43 Glossary.
The WRA Data Model
The WRA Data Model provides the instructions for how to digitally represent the configuration of an installed met mast, lidar, sodar,
floating lidar or solar measurement station. An implementation of the data model therefore can contain the latitude and
longitude of where the met mast was installed. At what height is a wind speed measurement been made and by what
sensor. How is this sensor mounted onto the mast. How the sensor is connected to the logger and how this logger channel
is programmed (i.e. what slope and offset values are programmed into the logger). And how all these properties
may have changed over time.
To learn more about the WRA Data Model, please read Amit Bohara's
introduction
in the Wiki section of this GitHub repository.
JSON (JavaScript Object Notation) is used to implement the WRA Data Model and
JSON Schema is used to express the WRA Data Model. The JSON Schema file, located at
./schema/iea43_wra_data_model.schema.json, is the
WRA Data Model. It can be thought of as a blueprint for how an implementation of the data model can be described with
the JSON data-interchange format. See Figure 1 below for a snippet of an example implementation.
Figure 1: Example implementation of the WRA Data Model.
The following tools are part of this undertaking:
-
WRA Data Model: This JSON Schema file is the WRA Data Model. It describes
how a JSON file that describes wind resource measurement data should be composed. -
Documentation: Markdown documentation for the WRA Data Model created directly from the JSON Schema.
-
Form App: This app shows a form that is modeled after the
JSON Schema and can create JSON data out of your inputs that is in accordance with the WRA Data Model. -
Python Data Model Loading Example: This notebook shows how to read an example file that
uses the WRA Data Model with Python.
Dissemination
The Task 43 team is actively working to disseminate information and improve user experience.
The Wiki tab in this GitHub repository is the starting
point to learn more via tutorials, recorded presentations, videos and more.
The Digital Calibration Certificate Data Model
As a requirement resulting from the development of the WRA Data Model, a
digital calibration certificate is introduced as a complementary standard. This
standard currently supports anemometer calibration certificates according to IEC 61400-12-1:2017.
Contributing to the Standard
We welcome all contributions including issue reporting, new features and bug fixes. For full details see the contributing
guidelines and other resources below:
- Contribution Guidelines
- Issue Log where you can ask for new features or inform us of any bugs.
- Kanban board showing what issues are been worked on, completed or yet to do.
- Meeting Notes of our regular meetings.
Getting Help
Please file a new issue in this repository with questions or concerns you might have. If you would like to chat directly with the contributors, please join our Slack channel at ieawinddigitalization.slack.com.
You can find out more about the IEA's Wind Task 43 working group at ieawindtask43.org.
Pipeline Status
| Pipeline | Status | Result |
|---|---|---|
| Documentation |  |
Documentation |
| Form App |  |
Form App |
Owner metadata
- Name: IEA Wind Digitalization (Task 43)
- Login: IEA-Task-43
- Email:
- Kind: organization
- Description: Github Repository for the efforts of IEA Wind task 43
- Website: www.ieawindtask43.org
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/57503085?v=4
- Repositories: 2
- Last ynced at: 2023-03-04T05:14:01.282Z
- Profile URL: https://github.com/IEA-Task-43
GitHub Events
Total
- Create event: 2
- Issues event: 9
- Watch event: 2
- Delete event: 3
- Member event: 1
- Issue comment event: 32
- Push event: 39
- Pull request review comment event: 12
- Pull request event: 8
- Pull request review event: 20
Last Year
- Create event: 2
- Issues event: 9
- Watch event: 2
- Delete event: 3
- Member event: 1
- Issue comment event: 32
- Push event: 39
- Pull request review comment event: 12
- Pull request event: 8
- Pull request review event: 20
Committers metadata
Last synced: 5 days ago
Total Commits: 479
Total Committers: 15
Avg Commits per committer: 31.933
Development Distribution Score (DDS): 0.395
Commits in past year: 38
Committers in past year: 4
Avg Commits per committer in past year: 9.5
Development Distribution Score (DDS) in past year: 0.289
| Name | Commits | |
|---|---|---|
| unknown | s****n@b****m | 290 |
| Heiko Westermann | h****n@w****e | 46 |
| Florian Roscheck | 4****f | 46 |
| GitHub Action | a****n@g****m | 41 |
| Jason Fields | v****e@y****m | 14 |
| Gibson | g****g@r****m | 14 |
| christianj | c****j@n****m | 8 |
| dependabot[bot] | 4****] | 6 |
| MatteoManaRWE | 1****E | 4 |
| Tom Clark | t****m@o****m | 2 |
| NikoSimisiroglou | 9****u | 2 |
| Michael Purdue | m****p@n****m | 2 |
| Guillermo Tornero | 6****o | 2 |
| Gibson Kersting | A****9@e****l | 1 |
| David Carlson | d****c@n****m | 1 |
Committer domains:
- nrgsystems.com: 2
- octue.com: 1
- naturalpower.com: 1
- rwe.com: 1
- github.com: 1
- windguard.de: 1
- brightwindanalysis.com: 1
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 163
Total pull requests: 120
Average time to close issues: 6 months
Average time to close pull requests: 18 days
Total issue authors: 26
Total pull request authors: 13
Average comments per issue: 4.94
Average comments per pull request: 1.63
Merged pull request: 105
Bot issues: 0
Bot pull requests: 6
Past year issues: 16
Past year pull requests: 13
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 24 days
Past year issue authors: 7
Past year pull request authors: 3
Past year average comments per issue: 2.0
Past year average comments per pull request: 3.08
Past year merged pull request: 6
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- stephenholleran (59)
- kersting (42)
- NikoSimisiroglou (8)
- Dynorat (7)
- heikowestermann (7)
- dancasey-ie (6)
- flrs-edf (3)
- MatteoManaRWE (3)
- jonssonchristian (3)
- abohara (3)
- agrangaraj (3)
- AndyClifton (3)
- SebAgain (2)
- kilojoules (2)
- sdsmdp (1)
Top Pull Request Authors
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- kersting (16)
- dependabot[bot] (6)
- Dynorat (4)
- NikoSimisiroglou (3)
- heikowestermann (3)
- flrs-edf (2)
- jonssonchristian (1)
- thclark (1)
- sdsmdp (1)
- GTornero (1)
- dave-c-vt (1)
- Speters1086 (1)
Top Issue Labels
- enhancement (63)
- question (24)
- bug (23)
- documentation (16)
- tool (7)
- breaking change (5)
- help wanted (5)
Top Pull Request Labels
- enhancement (25)
- documentation (9)
- bug (7)
- dependencies (6)
Dependencies
- 1471 dependencies
- cypress ^3.7.0 development
- react-scripts ^3.2.0 development
- start-server-and-test ^1.10.6 development
- @jsonforms/core ^2.4.0
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- @material-ui/icons ^4.5.1
- @types/jest ^24.0.23
- @types/lodash ^4.14.149
- @types/node ^12.12.14
- @types/react ^16.9.13
- @types/react-dom ^16.9.4
- @types/react-redux ^7.1.5
- lodash ^4.17.15
- react ^16.12.0
- react-dom ^16.12.0
- react-redux ^7.1.3
- redux ^4.0.4
- redux-devtools-extension ^2.13.8
- typescript ^3.7.2
- pandas <1.0
- requests *
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Score: 7.313220387090302