Stakeholder-specific Machine Learning support for designing sustainable buildings

Research Project, 2021 – 2026

The building sector is responsible for one-third of global greenhouse gas (GHG) emissions but also has one of the highest potentials for emission reduction. The early stage is the most optimal phase to conduct sustainability optimization as it requires minimum effort but could reach high improvements. However, this potential is rarely used today due to a lack of suitable support tools for different stakeholders such as architects and sustainability consultants. Advanced methods to assess environmental and economic performance exist today, but these expert tools are complex and time-consuming and not suitable for efficient optimization in the decisive early design phases. The project aims to develop an early-stage optimization workflow for Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) based on Machine Learning (ML) models to support architects in the design of more climate-friendly buildings.