Large-scale building age classification for urban energy demand estimation
Geo and satellite data for building age identification
O.M. Garbasevschi (TU Delft - Technology, Policy and Management)
W.K. Korthals Altes – Mentor (TU Delft - Land Development)
T. Verma – Mentor (TU Delft - Policy Analysis)
Iulia Lefter – Graduation committee member (TU Delft - System Engineering)
Michael Wurm – Mentor (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Jacob Estevam Schmiedt – Mentor (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
Björn Schiricke – Graduation committee member (Deutsches Zentrum für Luft- und Raumfahrt (DLR))
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Abstract
Urban areas are the biggest consumers of electricity and energy consumption is only likely to increase with rapid urbanization. Out of the urban building stock residential buildings require continuous supply of energy for space heating and appliances. To answer to this demand in a sustainable way policy maker need to design energy efficiency strategies that must rely on accurate and traceable models. These models estimate energy demand based on a series of building features, out of which building age is of prime importance because it predicts the insulation properties of the building. To support the energy modelling process, we propose a method of automatically identifying building age from spatial data at a large scale. We identify features of buildings that are significant for age prediction and determine which set of features has best prediction power at national scale, in Germany. It is expected that the accuracy of classification will be strongly related to sampling design and data availability. The final results will be used to identify the impact of misclassification errors on estimating energy use in urban energy models, providing in this manner a measure of the reliability of such models.