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Predictive mapping of the global power system using open data
Limited data on global power infrastructure makes it difficult to respond to challenges in electricity access and climate change. Although high-voltage data on transmission networks are often available, medium- and low-voltage data are often non-existent or unavailable. This presents a challenge for...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962213/ https://www.ncbi.nlm.nih.gov/pubmed/31941897 http://dx.doi.org/10.1038/s41597-019-0347-4 |
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author | Arderne, C. Zorn, C. Nicolas, C. Koks, E. E. |
author_facet | Arderne, C. Zorn, C. Nicolas, C. Koks, E. E. |
author_sort | Arderne, C. |
collection | PubMed |
description | Limited data on global power infrastructure makes it difficult to respond to challenges in electricity access and climate change. Although high-voltage data on transmission networks are often available, medium- and low-voltage data are often non-existent or unavailable. This presents a challenge for practitioners working on the electricity access agenda, power sector resilience or climate change adaptation. Using state-of-the-art algorithms in geospatial data analysis, we create a first composite map of the global power system with an open license. We find that 97% of the global population lives within 10 km of a MV line, but with large variations between regions and income levels. We show an accuracy of 75% across our validation set of 14 countries, and we demonstrate the value of these data at both a national and regional level. The results from this study pave the way for improved efforts in electricity modelling and planning and are an important step in tackling the Sustainable Development Goals. |
format | Online Article Text |
id | pubmed-6962213 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-69622132020-01-22 Predictive mapping of the global power system using open data Arderne, C. Zorn, C. Nicolas, C. Koks, E. E. Sci Data Data Descriptor Limited data on global power infrastructure makes it difficult to respond to challenges in electricity access and climate change. Although high-voltage data on transmission networks are often available, medium- and low-voltage data are often non-existent or unavailable. This presents a challenge for practitioners working on the electricity access agenda, power sector resilience or climate change adaptation. Using state-of-the-art algorithms in geospatial data analysis, we create a first composite map of the global power system with an open license. We find that 97% of the global population lives within 10 km of a MV line, but with large variations between regions and income levels. We show an accuracy of 75% across our validation set of 14 countries, and we demonstrate the value of these data at both a national and regional level. The results from this study pave the way for improved efforts in electricity modelling and planning and are an important step in tackling the Sustainable Development Goals. Nature Publishing Group UK 2020-01-15 /pmc/articles/PMC6962213/ /pubmed/31941897 http://dx.doi.org/10.1038/s41597-019-0347-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Arderne, C. Zorn, C. Nicolas, C. Koks, E. E. Predictive mapping of the global power system using open data |
title | Predictive mapping of the global power system using open data |
title_full | Predictive mapping of the global power system using open data |
title_fullStr | Predictive mapping of the global power system using open data |
title_full_unstemmed | Predictive mapping of the global power system using open data |
title_short | Predictive mapping of the global power system using open data |
title_sort | predictive mapping of the global power system using open data |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6962213/ https://www.ncbi.nlm.nih.gov/pubmed/31941897 http://dx.doi.org/10.1038/s41597-019-0347-4 |
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