Spatial factors influencing building age prediction and implications for urban residential energy modelling

O.M. Garbasevschi, Jacob Estevam Schmiedt, T. Verma, I. Lefter, W.K. Korthals Altes, Ariane Droin, Björn Schiricke, Michael Wurm

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Abstract

Urban energy consumption is expected to continuously increase alongside rapid urbanization. The building sector represents a key area for curbing the consumption trend and reducing energy-related emissions by adopting energy efficiency strategies. Building age acts as a proxy for building insulation properties and is an important parameter for energy models that facilitate decision making. The present study explores the potential of predicting residential building age at a large geographical scale from open spatial data sources in eight municipalities in the German federal state of North-Rhine Westphalia. The proposed framework combines building attributes with street and block metrics as classification features in a Random Forest model. Results show that the addition of urban fabric metrics improves the accuracy of building age prediction in specific training scenarios. Furthermore, the findings highlight the way in which the spatial disposition of training and test samples influences classification accuracy. Additionally, the paper investigates the impact of age misclassification on residential building heat demand estimation. The age classification model leads to reasonable errors in energy estimates, in various scenarios of training, which suggests that the proposed method is a promising addition to the urban energy modelling toolkit.
Original languageEnglish
Article number101637
Number of pages16
JournalComputers, Environment and Urban Systems
Volume88
DOIs
Publication statusPublished - 2021

Keywords

  • Open data
  • Random forest
  • Residential building age
  • Residential heat demand
  • Spatial autocorrelation
  • Urban morphology

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