Data validation

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#104 opened on Aug 11, 2023 0 comments 0 reactions 0 assignees View on GitHub

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Assessment

Difficulty: 5/5
Estimated time: Over a week
Newbie friendliness: 25/100

Issue type: Feature
Clarity: Needs clarification
Activity status: Stale
Tech stack: python
Domain: data

Research direction

No files, tests, or entry points are identified. Start by reviewing the PNNL storage database structure and the different technology classes, then define the metadata and visualization approach needed to compare essential parameters and limit validation to selected technologies; done means energy experts can inspect and validate the relevant data across classes.

Written by the indexing model from the issue text.

Description

feature

The techno-economical parameters of the different technologies are the main drivers of the PyPSA modeling.

As such, they should be easy to validate by energy experts with e.g. experience in building energy projects.
I structured the data from the PNNL storage database so that it would be easy to see which technologies PyPSA may select. This gives the opportunity to limit the data validation effort to a subset of technologies.

The database structure makes it difficult to do this for all the data, considering the different technology classes (storage (electricity, heat, ...) , generation, transport/transmission, ... ). An approach would be to use more meta-data that allows easy visualization of essential parameters across all technologies.

Dominant language: Python
Stars: 130
Forks: 59
PR merge metrics: No merged PRs in 30d

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