Complex Urban Systems ‒ LESO-PB ‐ EPFL

complex urban systems group thumb image Topic leader: Nahid Mohajeri (currently Visiting Fellow, University of Oxford)
Scientist: Roberto Castello
PhD students: Dan Assouline, Alina Walch

Factsheet 2017-2018

In order to model the dynamics of built environment and understand their sustainable development, as well as their interactions with infrastructure networks and urban ecosystem we need a comprehensive theoretical understanding of cities as complex systems. One principal aim of this research is to use the complex system theories and methods from physics and engineering in order to reduce the negative environmental impact of the cities through the following approaches: (1) developing energy-efficient urban forms, (2) modelling and identifying renewable energy resources from regional to city scale, (2) improving our understanding of urban metabolism, (3) improving the environmental impacts of urban infrastructure networks and mobility patterns through data-driven approaches and real-time data (4) assessing and minimising the ecological footprints of cities.

Research interests include:

Statistical modelling of the built environment
Ecological footprints of cities
Urban metabolism
Physics of urban form
GIS and spatial data analysis
Urban morphology and renewable energy
Transportation networks and mobility patterns

Current projects

HYENERGY – Hybrid renewable energy potential for the built environment using big data
SCCER FEEB&D Current potential of renewable energy sources
Integrating urban form and sociotehcnical potentials of decentralised energy supply for sustainable urban development (SNSV Mobility fellowship Dr N Mohajeri – host institute University of Oxford)

Past projects

Specific research topics

Recent publications

A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

N. Mohajeri; A. Walch; A. Smith; A. Gudmundsson; D. Assouline et al.

Sustainable Cities And Society. 2023-02-22. Vol. 92, p. 104451. DOI : 10.1016/j.scs.2023.104451.

A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

Spatio-temporal estimation of wind speed and wind power using extreme learning machines: predictions, uncertainty and technical potential

Impact of the COVID-19 pandemic on the energy performance of residential neighborhoods and their occupancy behavior

Shallow geothermal energy potential for heating and cooling of buildings with regeneration under climate change scenarios

Spatio-Temporal Estimation of Renewable Energy Potential in Built Environments using Big Data

Residential density classification for sustainable housing development using a machine learning approach

Using Machine Learning to estimate the technical potential of shallow ground-source heat pumps with thermal interference

An integrated microfluidic device for stem cell differentiation based on cell-imprinted substrate designed for cartilage regeneration in a rabbit model

Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty

Quantifying the technical geothermal potential from shallow borehole heat exchangers at regional scale

A fast machine learning model for large-scale estimation of annual solar irradiation on rooftops

Integrating urban form and distributed energy systems: Assessment of sustainable development scenarios for a Swiss village to 2050

A machine learning approach for mapping the very shallow theoretical geothermal potential

Spatio-temporal modelling and uncertainty estimation of hourly global solar irradiance using Extreme Learning Machines

A critical comparison of methods to estimate solar rooftop photovoltaic potential in Switzerland

Machine learning and geographic information systems for large-scale wind energy potential estimation in rural areas

Urban greening archetypes at the European scale

A solar-based sustainable urban design: The effects of city-scale street-canyon geometry on solar access in Geneva, Switzerland

Machine Learning and Geographic Information Systems for large-scale mapping of renewable energy potential

Combining Fourier Analysis And Machine Learning To Estimate The Shallow-Ground Thermal Diffusivity In Switzerland

Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests

A city-scale roof shape classification using machine learning for solar energy applications

Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration

Effects of city size on the large-scale decentralised solar energy potential

Extending building integrated photovoltaics (BiPV) using distributed energy hubs. A case study in Cartigny, Switzerland

Achieving energy sustainability in future neighborhoods through building refurbishment and energy hub concept: a case study in Hemberg-Switzerland

Quantifying rooftop photovoltaic solar energy potential: A machine learning approach

3D Model for Solar Energy Potential on Buildings from Urban LiDAR Data

Does roof shape matter? Solar photovoltaic (PV) integration on building roofs

How street canyon configuration control the accessibility of solar energy potential: Implication for urban design

Effects of urban compactness on solar energy potential

A machine learning methodology for estimating roof-top photovoltaic solar energy potential in Switzerland

Expansion and densification of cities: linking urban form to ecology

Expansion and densification of cities: linking urban form to urban ecology

Neighbourhood morphology and solar irradiance in relation to urban climate

How green are British cities? Scaling relations and CO2 emissions

Statistical-thermodynamics modelling of the built environment in relation to urban ecology

CO2 emissions in relation to street-network configuration and city size

Quantitative analysis of structural changes during rapid urban growth

Analysing and modelling the growth and efficiency of urban transportation infrastructures

Dike emplacement at Bardarbunga, Iceland, induces unusual stress changes, caldera deformation, and earthquakes

Analyzing Variation in Street Patterns: Implications for Urban Planning

The Evolution and Complexity of Urban Street Networks

Quantifying the Differences in Geometry and Size Distributions of Buildings Within Cities

Visualizing street orientation and solar radiation in relation to complex topography

Street networks in relation to landforms: Implications for fast-growing cities

Entropy and order in urban street networks

Modulation of Alzheimer’s pathology by cerebro-ventricular grafting of hybridoma cells expressing antibodies against Abeta in vivo