  Energy performance certificate (EPC) and thermal infrared (TIR) images both
play a key role in the energy performance mapping of the urban building stock.
In this paper, we developed parametric building archetypes using an EPC
database and conducted temperature clustering on TIR images acquired through
drones and satellite datasets. We evaluated 1725 EPCs of existing building
stock in Cambridge, UK to generate energy consumption profiles. Drone-based TIR
images of individual buildings in two Cambridge University colleges were
processed using a machine learning pipeline for thermal anomaly detection and
investigated the influence of two specific factors that affect the reliability
of TIR for energy management applications: ground sample distance (GSD) and
angle of view (AOV). The EPC-level results suggest that the construction year
of the buildings influences their energy consumption. For example, modern
buildings were over 30% more energy-efficient than older ones. Parallelly,
older buildings were found to show almost double the energy savings potential
through retrofitting than newly constructed buildings. TIR imaging results
showed that thermal anomalies can only be properly identified in images with a
GSD of 1m/pixel or less. A GSD of 1-6m/pixel can detect hot areas of building
surfaces. We found that a GSD > 6m/pixel cannot characterise individual
buildings but does help identify urban heat island effects. Additional
sensitivity analysis showed that building thermal anomaly detection is more
sensitive to AOV than to GSD. Our study informs newer approaches to building
energy diagnostics using thermography and supports decision-making for
retrofitting at a large scale.
