Learning Building Energy Efficiency with Semantic Attributes

Non-intrusive estimation of building energy efficiency has profound applications in advancing sustainability in the built environment. Recent studies often focus on predicting energy performance alone, neglecting the interplay between the performance and related building semantics. This paper investigates whether incorporating semantic attributes benefits energy efficiency estimation. We develop a neural network to estimate energy efficiency, with building age and usage type as additional supervision for multi-task learning. The neural network processes both aerial imagery and airborne LiDAR data to classify buildings as energy-efficient or inefficient. Our results demonstrate the effectiveness of the superimposed semantics, particularly with building age. With the multi-task model achieving a 63.78% F1 score and outperforming that supervised solely with energy efficiency by 2.86%, this paper reveals the potential of integrating semantic attributes in modeling building energy performance.