Exploring the effects of 2D/3D building factors on urban energy consumption using explainable machine learning

Abstract

The impact of various factors on building energy consumption (BEC) has garnered significant attention, as understanding these factors can help stakeholders optimize urban buildings' energy efficiency. However, most studies analyze these factors from a two-dimensional (2D) perspective (e.g., building materials, floor area ratio), overlooking the importance of emerging three-dimensional (3D) factors (e.g., building shading coefficient, building shape coefficient). This study aims to bridge this research gap by proposing a comprehensive framework to explore the impact of 2D/3D factors on urban BEC. The framework is validated using data from 818,199 buildings in New York City and employs an explainable machine learning method to reveal the contribution of 2D/3D factors to BEC. The results show that the importance of 2D and 3D factors for BEC is approximately 70 % and 30 %, respectively. Furthermore, this study considers the effects of building shade-related factors on BEC, finding their influence to be around 5–10 %. Based on the explainable artificial intelligence (AI) method results, a numerical dependency analysis is conducted for high-impact factors, providing decision-makers with a clearer understanding of how 2D/3D factors affect BEC. The novelty of this research lies in its comprehensive approach to investigating the impact of both 2D and 3D factors on urban BEC using advanced AI techniques. The findings offer valuable insights for urban building design and planning, particularly in the early stages, to promote energy efficiency. This study contributes to the growing body of knowledge on sustainable urban development and the application of advanced data analytics in building engineering.