Research LineSustainable smart cities and transport systems |
Team
| Atienza Alonso David |
| Hoffmann Patrik |
| Maréchal François |
| Paolone Mario |
Research Partners
 | UrbanTwin |
UrbanTwin aims to develop and validate a holistic tool to support decision-makers in achieving environmental goals, such as the Energy Strategy 2050 and the vision of climate-adaptive "sponge cities". The tool will be based on a detailed model of critical urban infrastructure, such as energy, water, buildings, and mobility, accurately simulating the evolution of these interlinked infrastructures under various climate scenarios and assessing the effectiveness of climate-change-related actions.
Urban areas are responsible for 75% of greenhouse gas emissions while rising temperatures significantly impact their liveability. They represent a natural integrator of several systems, including energy, water, buildings, and transport. So, they represent the ideal setting for implementing a coordinated, multi-sectoral response to climate changes leveraging digitalization as a systemic approach.
In UrbanTwin, we want to collect information from multiple sources by using new edge artificial intelligence (AI) platforms and integrate them using cloud computing technologies on a detailed model of critical urban infrastructures, such as energy, water (both clean and waste), buildings, and mobility and their inter-dependencies.
As a cutting-edge example of what digitalization and AI can offer, this tool will be able to consider underlying socio-economic and environmental factors, while assessing the effectiveness of climate-change-related actions beforehand. The goal is to develop a technology that is open and can be applied to other urban areas in any region of Switzerland.
UrbanTwin website
Related Publications
| Decomposition method to evaluate district heating/cooling network potential at urban scale | ||||
| Gouveia Braz, Ana Catarina; Briguet, Raphael; Girardin, Luc; Liu, Bingqian; Maréchal, François | ||||
| 2024-06-01 | Book of Abstract of the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering (ESCAPE34/PSE24), June 2-6, 2024, Florence, Italy | |||
| CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds | ||||
| Huang, Darong; Costero Valero, Luis Maria; Pahlevan, Ali; Zapater Sancho, Marina; Atienza Alonso, David | ||||
| 2024-01-23 | IEEE Transactions on Sustainable Computing |  |  | |