DecarbonAIte
Research Project , 2021 – 2024

Purpose and goal: The aim of this project is to adapt and apply ML algorithms to extract features from publicly available databases to enrich urban digital twin models and provide optimized renovation measures for decision-support.
First, the project will develop a ML-based method to extract information needed to simulate the performance of buildings. Second, an optimization method based on Genetic Algorithms will be developed that includes energy simulation, Life Cycle Assessment and a Life Cycle Cost Analysis. Third, the developed methods will be implemented in a decision-support tool. Expected results and effects: Two main outcomes are expected from this project. First, a scalable and future-proof workflow for enriching digital twins of cities with geometric features and semantic data. Second, the decision support tool will provide stakeholders, including real estate managers and municipalities with the right information for renovation planning.

Participants

Alexander Hollberg (contact)

Assistant Professor at Chalmers, Architecture and Civil Engineering, Building Technology

Alex Arnoldo Gonzalez Caceres

Postdoc at Chalmers, Architecture and Civil Engineering, Building Technology

Collaborations

Asymptotic AB

Göteborg, Sweden

Innovatum

Trollhättan, Sweden

Sinom AB

Göteborg, Sweden

Stiftelsen Chalmers Industriteknik

Gothenburg, Sweden

Funding

VINNOVA

Project ID: 2021-02759
Funding Chalmers participation during 2021–2024

Related Areas of Advance and Infrastructure

Sustainable development

Driving Forces

More information

Latest update

2022-04-28