pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Adam R. Jensen, Kevin S. Anderson, William F. Holmgren, Mark A. Mikofski, Clifford W. Hansen, Leland J. Boeman, Roel Loonen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python's iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH & ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).

Original languageEnglish (US)
Article number112092
JournalSolar energy
Volume266
DOIs
StatePublished - Dec 2023
Externally publishedYes

Keywords

  • Data article
  • Free and open-source software (FOSS)
  • Public data
  • Python
  • Solar energy

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • General Materials Science

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