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

Siddharth JoshiShivika MittalPaul HollowayPriyadarshi Ramprasad ShuklaBrian Ó GallachóirJames Glynn
Published in: Nature communications (2021)
Rooftop solar photovoltaics currently account for 40% of the global solar photovoltaics installed capacity and one-fourth of the total renewable capacity additions in 2018. Yet, only limited information is available on its global potential and associated costs at a high spatiotemporal resolution. Here, we present a high-resolution global assessment of rooftop solar photovoltaics potential using big data, machine learning and geospatial analysis. We analyse 130 million km2 of global land surface area to demarcate 0.2 million km2 of rooftop area, which together represent 27 PWh yr-1 of electricity generation potential for costs between 40-280 $ MWh-1. Out of this, 10 PWh yr-1 can be realised below 100 $ MWh-1. The global potential is predominantly spread between Asia (47%), North America (20%) and Europe (13%). The cost of attaining the potential is lowest in India (66 $ MWh-1) and China (68 $ MWh-1), with USA (238 $ MWh-1) and UK (251 $ MWh-1) representing some of the costliest countries.
Keyphrases
  • high resolution
  • machine learning
  • big data
  • artificial intelligence
  • mass spectrometry
  • social media
  • high speed