Work

Semantic building energy modeling: Analysis across geospatial scales

Year: 2025

Type: article

Abstract: Rapid decarbonization of the building sector is critical for mitigating climate change. While simulation-based stock level approaches such as urban building energy modeling (UBEM) have shown to be useful when developing carbon reduction plans, they have not reached their full potential convincing individual building owners to act: even though UBEMs are based on individual building simulations, they rely on archetypes averaged across multiple buildings, so saving predictions become unreliable at the building level. Meanwhile, at regional or national scales, as building stocks become more diverse, excessive efforts are required to accommodate the growing number of archetypes and handle patchworks of geographic information system (GIS) datasets. This paper introduces Semantic Building Energy Modeling (SBEM), a novel framework that replaces the static templates of UBEMs with problem-specific semantic building descriptions which are decoupled from model translation layers. By decoupling high-level, human-readable building features from computational representations, SBEM accommodates incomplete or probabilistic data and facilitates coordination between multiple teams, including construction experts, energy modelers, GIS experts, and software engineers. To illustrate the flexibility offered by SBEM, a case study was conducted modeling 2.5 million residential buildings in Massachusetts to assess the economic viability of heat pump adoption. The SBEM approach enables detailed, building-specific analyses, revealing significant variations in economic outcomes based on heating systems and regional characteristics. These insights underscore the importance of semantic granularity for individual homeowner decision-making. By providing a scalable and adaptable framework, SBEM can communicate and potentially overcome existing UBEM limitations, allowing consistent model use across scales.

Source: Building and Environment

DOI: 10.1016/j.buildenv.2025.112883

Authors: Samuel Wolk, Christoph Reinhart

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Sustainable cities and communities, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $4,280

Funders: U.S. Department of Defense, Massachusetts Institute of Technology