High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation

Though a global assessment of rooftop solar photovoltaic (RTSPV) technology’s potential and the cost is needed to estimate its impact, existing methods demand extensive data processing. Here, the authors report a machine learning method to realize a high-resolution global assessment of RTSPV potenti...

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Autores principales:	Siddharth Joshi, Shivika Mittal, Paul Holloway, Priyadarshi Ramprasad Shukla, Brian Ó Gallachóir, James Glynn
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/905a763f5fb74d2894def48ba62936ba
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spelling	oai:doaj.org-article:905a763f5fb74d2894def48ba62936ba2021-12-02T18:37:28ZHigh resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation10.1038/s41467-021-25720-22041-1723https://doaj.org/article/905a763f5fb74d2894def48ba62936ba2021-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25720-2https://doaj.org/toc/2041-1723Though a global assessment of rooftop solar photovoltaic (RTSPV) technology’s potential and the cost is needed to estimate its impact, existing methods demand extensive data processing. Here, the authors report a machine learning method to realize a high-resolution global assessment of RTSPV potential.Siddharth JoshiShivika MittalPaul HollowayPriyadarshi Ramprasad ShuklaBrian Ó GallachóirJames GlynnNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Siddharth Joshi Shivika Mittal Paul Holloway Priyadarshi Ramprasad Shukla Brian Ó Gallachóir James Glynn High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation
description	Though a global assessment of rooftop solar photovoltaic (RTSPV) technology’s potential and the cost is needed to estimate its impact, existing methods demand extensive data processing. Here, the authors report a machine learning method to realize a high-resolution global assessment of RTSPV potential.
format	article
author	Siddharth Joshi Shivika Mittal Paul Holloway Priyadarshi Ramprasad Shukla Brian Ó Gallachóir James Glynn
author_facet	Siddharth Joshi Shivika Mittal Paul Holloway Priyadarshi Ramprasad Shukla Brian Ó Gallachóir James Glynn
author_sort	Siddharth Joshi
title	High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation
title_short	High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation
title_full	High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation
title_fullStr	High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation
title_full_unstemmed	High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation
title_sort	high resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/905a763f5fb74d2894def48ba62936ba
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High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation

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