A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics
Abstract This paper presents an open-source dataset intended to enhance the analysis and optimization of photovoltaic (PV) power generation in urban environments, serving as a valuable resource for various applications in solar energy research and development. The dataset comprises measured PV power...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04397-y |
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| author | Zinan Lin Qi Zhou Zhe Wang Ce Wang Davis Boyd Bookhart Marcus Leung-Shea |
| author_facet | Zinan Lin Qi Zhou Zhe Wang Ce Wang Davis Boyd Bookhart Marcus Leung-Shea |
| author_sort | Zinan Lin |
| collection | DOAJ |
| description | Abstract This paper presents an open-source dataset intended to enhance the analysis and optimization of photovoltaic (PV) power generation in urban environments, serving as a valuable resource for various applications in solar energy research and development. The dataset comprises measured PV power generation data and corresponding on-site weather data gathered from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals at the inverter-level. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using the Brick schema, which simplifies data comprehension and the development of smart analytics applications. This paper provides a detailed description on the site specifications, data collection method, data records, and data validation. This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems. |
| format | Article |
| id | doaj-art-63e7943b348c4517bb77ad19dc03e76f |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-63e7943b348c4517bb77ad19dc03e76f2025-01-19T12:10:00ZengNature PortfolioScientific Data2052-44632025-01-0112111310.1038/s41597-025-04397-yA high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analyticsZinan Lin0Qi Zhou1Zhe Wang2Ce Wang3Davis Boyd Bookhart4Marcus Leung-Shea5Department of Civil and Environmental Engineering, The Hong Kong University of Science and TechnologyDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and TechnologyDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and TechnologyDepartment of Civil and Environmental Engineering, The Hong Kong University of Science and TechnologySustainability/Net-Zero Office, The Hong Kong University of Science and TechnologySustainability/Net-Zero Office, The Hong Kong University of Science and TechnologyAbstract This paper presents an open-source dataset intended to enhance the analysis and optimization of photovoltaic (PV) power generation in urban environments, serving as a valuable resource for various applications in solar energy research and development. The dataset comprises measured PV power generation data and corresponding on-site weather data gathered from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals at the inverter-level. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using the Brick schema, which simplifies data comprehension and the development of smart analytics applications. This paper provides a detailed description on the site specifications, data collection method, data records, and data validation. This dataset can be used in various applications - PV generation benchmarking, PV degradation analysis, PV fault detection, solar radiation and PV power generation forecasting, and the simulation and design of PV systems.https://doi.org/10.1038/s41597-025-04397-y |
| spellingShingle | Zinan Lin Qi Zhou Zhe Wang Ce Wang Davis Boyd Bookhart Marcus Leung-Shea A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics Scientific Data |
| title | A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics |
| title_full | A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics |
| title_fullStr | A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics |
| title_full_unstemmed | A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics |
| title_short | A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics |
| title_sort | high resolution three year dataset supporting rooftop photovoltaics pv generation analytics |
| url | https://doi.org/10.1038/s41597-025-04397-y |
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