OpenSolar: Promoting the openness and accessibility of diverse public solar datasets. (August 2019)
- Record Type:
- Journal Article
- Title:
- OpenSolar: Promoting the openness and accessibility of diverse public solar datasets. (August 2019)
- Main Title:
- OpenSolar: Promoting the openness and accessibility of diverse public solar datasets
- Authors:
- Feng, Cong
Yang, Dazhi
Hodge, Bri-Mathias
Zhang, Jie - Abstract:
- Highlights: Developing an R and Python package to enhance the openness and accessibility of publicly available solar datasets. Providing access to solar data with multiple types, including sky images, behind-the-meter photovoltaic generation, etc. Demonstrating the package usage by self-contained scripts. 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, inHighlights: Developing an R and Python package to enhance the openness and accessibility of publicly available solar datasets. Providing access to solar data with multiple types, including sky images, behind-the-meter photovoltaic generation, etc. Demonstrating the package usage by self-contained scripts. 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. … (more)
- Is Part Of:
- Solar energy. Volume 188(2019)
- Journal:
- Solar energy
- Issue:
- Volume 188(2019)
- Issue Display:
- Volume 188, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 188
- Issue:
- 2019
- Issue Sort Value:
- 2019-0188-2019-0000
- Page Start:
- 1369
- Page End:
- 1379
- Publication Date:
- 2019-08
- Subjects:
- Solar data openness -- R -- Python -- Machine learning -- Data-driven
Solar energy -- Periodicals
Solar engines -- Periodicals
621.47 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0038092X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.solener.2019.07.016 ↗
- Languages:
- English
- ISSNs:
- 0038-092X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 8327.200000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 16296.xml