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dc.contributor.authorStowell, Den_US
dc.contributor.authorKelly, Jen_US
dc.contributor.authorTanner, Den_US
dc.contributor.authorTaylor, Jen_US
dc.contributor.authorJones, Een_US
dc.contributor.authorGeddes, Jen_US
dc.contributor.authorChalstrey, Een_US
dc.date.accessioned2020-12-02T09:57:18Z
dc.date.available2020-10-20en_US
dc.date.issued2020-11-13en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/68887
dc.description.abstractSolar photovoltaic (PV) is an increasingly significant fraction of electricity generation. Efficient management, and innovations such as short-term forecasting and machine vision, demand high-resolution geographic datasets of PV installations. However, official and public sources have notable deficiencies: spatial imprecision, gaps in coverage and lack of crucial meta data, especially for small-scale solar panel installations. We present the results of a major crowd-sourcing campaign to create open geographic data for over 260,000 solar PV installations across the UK, covering an estimated 86% of the capacity in the country. We focus in particular on capturing small-scale domestic solar PV, which accounts for a significant fraction of generation but was until now very poorly documented. Our dataset suggests nameplate capacities in the UK (as of September 2020) amount to a total of 10.66 GW explicitly mapped, or 13.93 GW when missing capacities are inferred. Our method is applied to the UK but applicable worldwide, and compatible with continual updating to track the rapid growth in PV deployment.en_US
dc.format.extent394 - ?en_US
dc.languageengen_US
dc.relation.ispartofSci Dataen_US
dc.rightsCreative Commons Attribution 4.0 International License
dc.titleA harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK.en_US
dc.typeArticle
dc.identifier.doi10.1038/s41597-020-00739-0en_US
pubs.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/33188212en_US
pubs.issue1en_US
pubs.notesNot knownen_US
pubs.publication-statusPublished onlineen_US
pubs.volume7en_US
dcterms.dateAccepted2020-10-20en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderEnabling worldwide solar PV nowcasting via machine vision and open data::The Alan Turing Instituteen_US


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