Computational Modeling of Land Surface Temperature Using Remote Sensing Data to Investigate the Spatial Arrangement of Buildings and Energy Consumption Relationship

EasyChair Preprint no. 2401

Abstract

The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighbourhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively.

Keyphrases: energy consumption, Land surface temperature, remote sensing, residential buildings, urban morphology, urban sustainability

@Booklet{EasyChair:2401,
  author = {Maryam Faroughi and Mehrdad Karimimoshaver and Farshid Aram and Ebrahim Solgi and Amir Mosavi and Narjes Nabipour and Kwok-Wing Chau},
  title = {Computational Modeling of Land Surface Temperature Using Remote Sensing Data to Investigate the Spatial Arrangement of Buildings and Energy Consumption Relationship},
  howpublished = {EasyChair Preprint no. 2401},

  year = {EasyChair, 2020}}