Work

Geospatial clustering as a method to reduce the computational load in urban building energy simulation

Year: 2025

Type: article

Abstract: Since the recent birth of physics-based urban building energy modeling (UBEM), researchers have started tackling the issues characterizing this research field, mainly linked to the lack of extensive and standardized building information datasets and the necessity of simplifying the modeling process. Concerning the latter, geospatial clustering approaches seem to be plausible methods to reduce the computational load in urban simulation, and this work aims to test their suitability and performance. For this purpose, a case study of almost 3800 buildings in Padova, Italy, is analyzed. The tendency analysis is first used to quantify the underlying clusters that could be present. The study of this metric reveals the organic morphology and the heterogeneity of building stock in European cities like Padova. Additionally, several clustering algorithms are applied to the location, use, envelope, and geometry variables to simulate building clusters and quantify the increase in geometric and heating/cooling demand uncertainty. Results show that, for this case study, building clusters are characterized by lower volumes than when considering single buildings, which is also reflected in a lower heating and cooling demand prediction. Nonetheless, these errors are found to be in an acceptable range (less than 6%) for UBEM applications.

Source: Sustainable Cities and Society

DOI: 10.1016/j.scs.2025.106247

Authors: Mohamad Hasan Khajedehi, Enrico Prataviera, Sara Bordignon, Angelo Zarrella, Michele De Carli

Institution: University of Padua

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,870