Urban morphology indicator analyzes for urban energy modeling

Hung-Chu Chen (Corresponding author), Qi Han, Bauke de Vries

Research output: Contribution to journalArticleAcademicpeer-review

33 Citations (Scopus)

Abstract

The reduction of energy consumption in the urban environment has attracted numerous studies in recent decades. However, limited studies have been conducted to investigate the impact of urban morphology on urban energy demand. Eindhoven serves as a case study in this paper to analyze the spatial relation between energy demand and urban morphology indicators (UMIs). UMIs are calculated by Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), building height, plot area, and building volume. The energy demand is calculated using annual gas and electricity demand. The ordinary least squares regression (OLS) and geographically weighted regression (GWR) models are applied to find the relation between energy demand and the UMIs. For the spatial regression, land-use is clustered into five types: urban areas (U), open space (O), artificial green areas (G), natural green areas (V), and water body (W). The results revealed that the energy demand and UMIs have a significant spatial relation. Urban energy demand as a dependent variable is best described by a combination of NDVI, building height, and plot area as the independent variables. The developed model can support local authorities in developing sustainable strategies and efficient energy planning.

Original languageEnglish
Article number101863
Number of pages10
JournalSustainable Cities and Society
Volume52
Early online date27 Sept 2019
DOIs
Publication statusPublished - 1 Jan 2020

Funding

The authors would like to express their gratitude to the United States Geographical Survey (USGS), Dutch Central Bureau Statistics (CBS), and Royal Netherlands Meteorological Institute (KNMI) for providing open data. The authors also kindly thank the Ministry of Education in Taiwan , Deltas Electronics Foundation , and Netherlands Trade & Investment Office (NTIO) for their funding to this project. Finally, the authors would like to thank the supervisors and colleagues from Urban Systems and Real Estate group in the Department of the Built Environment, Eindhoven University of Technology for their precious time and contributions to this project.

Keywords

  • Energy demand model
  • Geographically weighted regression (GWR)
  • Normalized difference built-up index (NDBI)
  • Normalized difference vegetation index (NDVI)
  • Urban morphology

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