Solar Data Tools pre-submission inquiry

[pluflou]

Submitting Author: Name (@pluflou)
Package Name: Solar Data Tools One-Line Description of Package: Library of tools for analyzing photovoltaic power time-series data. Repository Link (if existing): https://github.com/slacgismo/solar-data-tools EiC: @cmarmo

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Code of Conduct & Commitment to Maintain Package

• [x] I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
• [x] I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

Description

• Include a brief paragraph describing what your package does: Solar Data Tools is an open-source Python library for analyzing PV power (and irradiance) time-series data. It provides methods for data I/O, cleaning, filtering, plotting, and analysis. These methods are largely automated and require little to no input from the user regardless of system type—from utility tracking systems to multi-pitch rooftop systems. Solar Data Tools was developed to enable analysis of unlabeled PV data, i.e. with no model, no meteorological data, and no performance index required, by taking a statistical signal processing approach in the algorithms used in the package’s main data processing pipeline.

Community Partnerships

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• [ ] Astropy: My package adheres to Astropy community standards
• [ ] Pangeo: My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook

Scope

• Please indicate which category or categories this package falls under:

  • [x] Data retrieval
  • [x] Data extraction
  • [x] Data processing/munging
  • [ ] Data deposition
  • [ ] Data validation and testing
  • [x] Data visualization
  • [ ] Workflow automation
  • [ ] Citation management and bibliometrics
  • [ ] Scientific software wrappers
  • [ ] Database interoperability

Domain Specific

• [ ] Geospatial
• [ ] Education

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• Explain how and why the package falls under these categories (briefly, 1-2 sentences). For community partnerships, check also their specific guidelines as documented in the links above. Please note any areas you are unsure of:

The IO module provides functions to pull data from various online sources. The main DataHandler class provides automated data extraction and cleaning of unlabeled time-series data (including cleaning up erroneous timestamps from measurement devices), provides a data quality score, and has methods to run several analyses on the data such as time shift analysis and degradation loss factor analysis. The scientific methods used in this package have been peer reviewed in other publications/conferences. See here and here.

• Who is the target audience and what are the scientific applications of this package?

This package is for anyone dealing with photovoltaic data, especially data with no meteorological information (unlabeled). This includes photovoltaic professionals (in private solar industry or utility companies for example), researchers and students in the solar power domain, community solar owners, and anyone with a rooftop system. The scientific goal of the package is to facilitate analysis of photovoltaic data for any system, even those that are difficult to model, and the package uses signal decomposition to achieve that.

• Are there other Python packages that accomplish similar things? If so, how does yours differ?

There are two other packages that are similar in that they offer data analysis tools for solar applications: PVAnalytics and RdTools. They are both model driven, and require the user to define their own analysis. PVAnalytics focuses on preprocessing and QA, while RdTools focuses on loss factor analysis. Solar Data Tools provides both data quality and loss factor analysis, runs automatically with little to no setup, and is model-free and does not require any weather or other information. Solar Data Tools is most suited for when users want a pre-defined pipeline to get information on complex systems/sites that can't be modeled easily and that no meteorological data. A recent tutorial that was part of a virtual tutorial series on open-source tools and open-access solar data held by DOE’s Solar Technology Office in March 2024 goes over the differences in these packages and when each tool is appropriate to use. You can find the recording here and the slide deck here (see slide 16 for a summary).

• Any other questions or issues we should be aware of:

P.S. Have feedback/comments about our review process? Leave a comment here

[cmarmo]

Dear @pluflou, thank you for your detailed submission to pyOpenSci! Solar Data Tools is definitely in scope for us: you provided relevant and very interesting references. Would you mind opening a new submission issue referencing this presubmission enquiry? Thank you.

[pluflou]

Dear @pluflou, thank you for your detailed submission to pyOpenSci! Solar Data Tools is definitely in scope for us: you provided relevant and very interesting references. Would you mind opening a new submission issue referencing this presubmission enquiry? Thank you.

Thank you, will do! We're just going to release a new version with some bug fixes before submitting. Should be within the month.