Automated building energy consumption estimation from aerial imagery

Artem Streltsov, Kyle Bradbury, Jordan Malof

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a methodology for automatically estimating the energy consumption of buildings from aerial imagery using data from Gainesville, Florida. By detecting buildings in the imagery using convolutional neural networks and extracting features from those building annotations, we use only imagery-derived features to estimate building energy consumption using random forests regression. For individual buildings, we achieve a predictive R 2 value of 0.26, and with spatial aggregation over an area of 400m×400m our predictive R 2 value increases to 0.95. We also explore the sensitivity of these estimates to errors in the building estimation process. Our results indicate that information limited to the size and shape of buildings, provides substantial predictive potential for the energy consumption of buildings.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1676-1679
Number of pages4
ISBN (Electronic)9781538671504
DOIs
StatePublished - Oct 31 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: Jul 22 2018Jul 27 2018

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2018-July

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/TerritorySpain
CityValencia
Period07/22/1807/27/18

Keywords

  • Aerial imagery
  • Building detection
  • Energy consumption
  • Machine learning

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