Extracting Building Characteristics Essential for Building Energy Consumption Predictions: Learning from façade Images through Deep Learning

Xinran Yu, Semiha Ergan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Buildings account for 40% of the energy consumption and more than 50% of the greenhouse gas (GHG) emissions worldwide. Various building energy efficiency policies have been issued in many cities around the US, requiring the disclosure of buildings' energy performance and encourage building owners to take this information into consideration for retrofit investment decisions. However, various types of building information at different scales are meant to be collected and disclosed with costs that have substantial variations accordingly. Hence, complying with these policies is expensive and time-consuming for both government agencies and building owners. This paper proposes a non-intrusive and scalable data-driven approach to automatically capture energy-critical building characteristics to declare energy benchmarking or retrofitting policies. This approach first integrated open city data sets (building-related) and identified principal building variables in relation to energy performance indexes (i.e., electricity usage), then utilized transfer learning to retrain deep learning models on building façade images (retrieved from Google Street View) to extract their corresponding principal building variables. To elaborate, given a fully captured façade image of any building, this approach provides the data values for the identified principal variables (e.g., for a façade image given as an input, the outputs will include buildingType = Office). The best accuracy of the extraction model exceeds 80% in extracting building types from images, which indicates that the model is capable of extracting data values for the principal building variables that are essential for energy performance of buildings.

Original languageEnglish (US)
Title of host publicationComputing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021
EditorsR. Raymond A. Issa
PublisherAmerican Society of Civil Engineers (ASCE)
Pages131-139
Number of pages9
ISBN (Electronic)9780784483893
DOIs
StatePublished - 2021
Event2021 International Conference on Computing in Civil Engineering, I3CE 2021 - Orlando, United States
Duration: Sep 12 2021Sep 14 2021

Publication series

NameComputing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021

Conference

Conference2021 International Conference on Computing in Civil Engineering, I3CE 2021
Country/TerritoryUnited States
CityOrlando
Period9/12/219/14/21

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications

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