OpenSolar: Promoting the Openness and Accessibility of Diverse Public Solar Datasets

Brian Hodge, Cong Feng, Dazhi Yang, Jie Zhang

Research output: Contribution to journalArticlepeer-review

31 Scopus Citations

Abstract

Observational solar data is the foundation of data-driven research in solar power grid integration and power system operations. Compared to other fields in data science, the openness and accessibility of solar data is lacking, which prevents solar data science from catching up with the emerging trends of data science (e.g., deep learning). In this paper, OpenSolar, a package with both R and Python versions, is developed to enhance the openness and accessibility of publicly available solar datasets. The OpenSolar package provides access to multiple types of solar data, primarily from four datasets: (1) the National Renewable Energy Laboratory (NREL) Solar Power Data for Integration Studies dataset, (2) the NREL Solar Radiation Research Laboratory dataset, (3) the Sheffield Solar-Microgen database, and (4) the Dataport database. Unlike other open solar datasets that only contain meteorological data, the four datasets in the OpenSolar package also contain behind-the-meter power data, sky images, and solar power data with satisfactory temporal and spatial resolution and coverage. The overview, quality-control methods, and potential usage of the datasets, in conjunction with sample code implementing the OpenSolar functions, are described in this paper. The package is expected to assist in bridging the gaps between the research fields of solar energy, power systems, and data science.
Original languageAmerican English
Pages (from-to)1369-1379
Number of pages11
JournalSolar Energy
Volume188
DOIs
StatePublished - 2019

NREL Publication Number

  • NREL/JA-5D00-74587

Keywords

  • data-driven
  • machine learning
  • Python
  • R
  • solar data openness

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