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 language | English |
|---|---|
| Article number | 112092 |
| Number of pages | 9 |
| Journal | Solar Energy |
| Volume | 266 |
| DOIs | |
| Publication status | Published - Dec 2023 |
Funding
The author would like to acknowledge Afshin Andreas (NREL) for providing a list of MIDC stations, Josh Peterson (University of Oregon) for providing a list of SRML stations, Howard Diamond (CRN) for providing background information on the CRN network, and Amelie Driemel (BSRN data curator) for providing insight into the BSRN. Additionally, Marion Schroedter-Homscheidt (CAMS) was extremely helpful in providing additional information on the CAMS Radiation Service, and Nikos Alexandris (Joint Research Centre) provided data on the PVGIS geographical coverage. Adam R. Jensen was supported by the Danish Energy Agency under grant numbers 64020-1082 and 134232-510237. Part of the work was also carried out through participation in the 2021 Google Summer of Code program. Kevin S. Anderson and Clifford W. Hansen were supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number 38267. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. This material is based upon work supported by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy under the Solar Energy Technologies Office (SETO) and DE-FOA-0001649, Award Number DE-EE0008214.
| Funders | Funder number |
|---|---|
| U.S. Department of Energy | |
| Office of Energy Efficiency and Renewable Energy | |
| National Nuclear Security Administration | DE-FOA-0001649, DE-EE0008214, DE-NA0003525 |
| University of Oregon | |
| Solar Energy Technologies Office | 38267 |
| Chinese Academy of Medical Sciences | |
| Energistyrelsen | 134232-510237, 64020-1082 |
Keywords
- Data article
- Free and open-source software (FOSS)
- Public data
- Python
- Solar energy
