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利用公开数据对全球电力系统进行预测性建模。

Predictive mapping of the global power system using open data.

机构信息

World Bank Group, Washington, D.C., USA.

Environmental Change Institute, University of Oxford, Oxford, UK.

出版信息

Sci Data. 2020 Jan 15;7(1):19. doi: 10.1038/s41597-019-0347-4.

Abstract

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.

摘要

有关全球电力基础设施的资料有限,这使得应对电力供应和气候变化方面的挑战变得困难。尽管传输网络的高压数据通常是可用的,但中压和低压数据通常不存在或无法获得。这给从事电力供应议程、电力部门弹性或适应气候变化工作的从业人员带来了挑战。我们使用地理空间数据分析的最先进算法,创建了一个带有开放许可证的全球电力系统复合图。我们发现,全球 97%的人口居住在距离中压线路 10 公里以内的地方,但地区和收入水平之间存在很大差异。在我们的 14 个国家验证集中,我们的准确率达到了 75%,并展示了这些数据在国家和地区层面的价值。本研究的结果为改进电力建模和规划工作铺平了道路,是实现可持续发展目标的重要一步。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0d99/6962213/7ac333bafdcd/41597_2019_347_Fig1_HTML.jpg

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