Urban solar utilization potential mapping via deep learning technology: A case study of Wuhan, China

Zhaojian Huang, Thushini Mendis, Shen Xu

Research output: Journal PublicationArticlepeer-review

33 Citations (Scopus)

Abstract

This study presents a novel approach to detect the city-wide solar potential which utilizes image segmentation with deep learning technology unlike traditional methods. In order to study the solar energy potential in the urban scale, there exists a requirement to quantify the roof area of buildings which are available to receive solar radiation, calculate the total solar radiation obtained within the region based on the meteorological conditions, and determine the total solar energy potential with carbon emissions savings and the economic recovery period. However, obtaining the overall roof area of a city is an existing difficulty when considering the quantification of solar potential in the urban scale. This study utilizes the U-Net of deep learning technology, and a large range of satellite maps to identify the building roof, in order to estimate the city's solar potential. This research established that the urban roofs of Wuhan have an annual photovoltaic electricity generation potential of 17292.30 × 106 kWh/year.

Original languageEnglish
Pages (from-to)283-291
Number of pages9
JournalApplied Energy
Volume250
DOIs
Publication statusPublished - 15 Sep 2019
Externally publishedYes

Keywords

  • Deep learning
  • Neural networks
  • Solar energy
  • Solar potential mapping
  • Urban energy

ASJC Scopus subject areas

  • Building and Construction
  • Mechanical Engineering
  • Energy (all)
  • Management, Monitoring, Policy and Law

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