A geospatial approach of downscaling urban energy consumption density in mega-city Dhaka, Bangladesh

Sujit Kumar Sikder, Magesh Nagarajan, Shiba Kar, Theo Koetter

Research output: Contribution to journalArticle

6 Citations (Scopus)
30 Downloads (Pure)

Abstract

Lack of energy consumption data limits resource optimized urban structure and energy planning in developing countries like Bangladesh. Focusing on mega-city Dhaka as a case, this study applies a geospatial approach of using multi-source national and regional datasets and visual analytics to downscale and estimate energy consumption at a local scale (such as ward and gridcell). The energy consumption density (ECD), as a measure of end energy use in a unit area, was estimated and mapped by linking building floorspace data with residents’ energy use indicators such as per capita energy consumption, household energy expenditure, and mobility (transportation) pattern. This study also evaluated the ECD modelling outputs, and their sensitivity to distance from central business district (CBD) and total building floorspace. Results found a positive correlation between the residential building floorspace and estimated ECD. Regression and sensitivity analysis also identified and mapped significant spatial clusters and outliers in estimated ECD pattern of Dhaka city. This approach and methodology could help similar cities in other developing countries adopt and implement energy focused urban development.
Original languageEnglish
Pages (from-to)10-30
Number of pages21
JournalUrban Climate
Volume26
Early online date15 Aug 2018
DOIs
Publication statusPublished - Dec 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Urban Climate. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Urban Climate,Vol 26, 2018. DOI: 10.1016/j.uclim.2018.08.004

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Spatial modelling
  • Geo-spatial modelling
  • Urban energy
  • Energy consumption density
  • GIS
  • Spatial analysis
  • Energy and Mobility

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