TY - JOUR
T1 - Spatial factors influencing building age prediction and implications for urban residential energy modelling
AU - Garbasevschi, O.M.
AU - Estevam Schmiedt, Jacob
AU - Verma, T.
AU - Lefter, I.
AU - Korthals Altes, W.K.
AU - Droin, Ariane
AU - Schiricke, Björn
AU - Wurm, Michael
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Open data
KW - Random forest
KW - Residential building age
KW - Residential heat demand
KW - Spatial autocorrelation
KW - Urban morphology
UR - http://www.scopus.com/inward/record.url?scp=85104689453&partnerID=8YFLogxK
U2 - 10.1016/j.compenvurbsys.2021.101637
DO - 10.1016/j.compenvurbsys.2021.101637
M3 - Article
SN - 0198-9715
VL - 88
JO - Computers, Environment and Urban Systems
JF - Computers, Environment and Urban Systems
M1 - 101637
ER -