Urban data mining, intelligence and simulation

Scientific project leaders: Dasaraden Mauree, Roberto Castello
Postdoctoral fellow: Silvia Coccolo
PhD students: Amarasinghage Tharindu Dasun Perera, Alina Walch, Dan Assouline
External: Vahid Nik (Chalmers/Lund Univ.), Nahid Mohajeri (Oxford Univ.)

The group focuses on understanding urban systems through their physical processes, the modelling of their dynamics and of the energy potential in order to improve their environmental sustainability. Multiple approaches and techniques are used: (i) statistical analysis of urban data collected in situ or secondary datasets from clouds and national databases, (ii) usage of data driven and machine learning techniques for forecasting, classification and automated recognition of urban elements, (iii) simulation of environmental phenomena and energy fluxes using advanced modelling tools.

More specifically, urban systems, generally consisting of several building blocks, are analysed either by simulation or by measurement to account for the numerous interactions happening between the elementary building objects and their environment. These interactions can be radiative (with the exchange of shortwave and longwave), conductive and convective (through the exchange of heat) but can also relate to the impact of built surfaces on the air flow and trapping of heat in urban areas. The group also works on the integration of decentralized energy systems in urban areas by looking at the energy flows.

Due to the complexity of urban areas, machine-learning techniques and simulation tools are developed and used to maintain a balance between accuracy and computational time. The different monitoring campaigns provide useful data to validate and improve the models. Our research focuses on modelling the potential of renewable technologies (solar, wind and geothermal) in the built environment in relation to the variable nature of energy resources. Using the existing large amount of data from local communities and at the national/regional level, complemented by satellite imagery and remote sensing, we aim at performing accurate spatio-temporal assessment of renewable energy potential. For this, we combine state-of-the-art deterministic models, machine Learning techniques and data-driven methods and we use them to model the uncertainty associated to the predictions and to forecast long-term evolutions of the urban systems.

Research interests

Data and methodologies


  • Measurement of Turbulence in an Urban Setup (MoTUS)
  • Data collection campaigns
  • Satellite imagery analysis (MeteoSuisse)
  • Environmental and socio-economic secondary datasets
  • National and local surveys

Machine learning techniques

  • Estimate and forecast of RE potential at high spatial and temporal resolution
  • Classification and clustering of roofs, building types, district archetypes
  • Fast and efficient modelling of wind speed and stochastic phenomena in the built environment
  • Prediction uncertainty on forecasted values
  • Supervised and semi-supervised neural networks for satellite/aerial image processing

Deterministic modeling

  • Climate change and Urban climate
  • Energy demand
  • Outdoor thermal comfort
  • Energy systems

Current projects


Past research projects

See publications by former topic leaders Nahid Mohajeri / Jérôme Kämpf / Darren Robinson

  • The complexity of roof-shape and solar energy
  • Street canyon and solar energy
  • Data Mining: Geo-Dependent Energy Supply in Relation to Urban Form
  • Urban Configuration (Building Typology and Mobility Patterns) and Renewable Energy
  • Urban Metabolism: From Energy Flow to Energy Management
  • Integration of decentralized energy adaptive systems for cites (IDEAS4cities)
  • Innovative planning and management instruments of urban energy systems (QUAD – sustainable districts)
  • Sustainable cities and urban energy systems of the future: Urban multiscale energy modelling (UMEM)
  • Investigation of strategies leading to a 2000W city using bottom-up models of urban energy flows (2 kW)
  • Ecosystemic modelling of urban metabolism based on modern thermodynamics
  • Multiscale modelling of building-urban interactions: understanding, modelling and mitigating the heat island effect

PhD theses

7756 2017 Silvia Coccolo Bioclimatic Design of Sustainable Campuses using Advanced Optimisation Methods
6102 2014 Diane Perez A framework to model and simulate the disaggregated energy flows supplying buildings in urban areas
5673 2013 Urs Wilke Probabilistic Bottom-Up Modelling of Occupancy and Activities to Predict Electricity Demand in Residential Buildings >> Detailed thesis results (for access password please contact [email protected])
4587 2010 Frédéric Haldi On the unification of behavioural modelling, human comfort and energy simulation in buildings
4657 2010 Marylène Montavon Optimisation of Urban Form by the Evaluation of the Solar Potential
4548 2009 Jérôme Kämpf On the modelling and optimisation of urban energy fluxes
4531 2009 Adil Rasheed Multiscale modelling of urban climate


K. Siraganyan; A. Perera; J.-L. Scartezzini; D. Mauree : Eco-Sim: A Parametric Tool to Evaluate the Environmental and Economic Feasibility of Decentralized Energy Systems; Energies. 2019-02-26. DOI : 10.3390/en12050776.
L. Telesca; M. Laib; F. Guignard; D. Mauree; M. Kanevski et al. : Linearity versus non-linearity in high frequency multilevel wind time series measured in urban areas; Chaos, Solitons & Fractals. 2019. DOI : 10.1016/j.chaos.2019.02.002.
S. Torabi Moghadam; S. Coccolo; G. Mutani; P. Lombardi; J.-L. Scartezzini et al. : A new clustering and visualization method to evaluate urban heat energy planning scenarios; Cities. 2019-05-19. DOI : 10.1016/j.cities.2018.12.007.
F. Guignard; D. Mauree; M. Lovallo; M. Kanevski; L. Telesca : Fisher–Shannon Complexity Analysis of High-Frequency Urban Wind Speed Time Series; Entropy. 2019-01-10. DOI : 10.3390/e21010047.
S. T. Moghadam; S. Coccolo; G. Mutani; P. Lombardi; J. L. Scartezzini et al. : A new clustering and visualization method to evaluate urban energy planning scenarios. 2018. DOI : 10.31224/osf.io/b9znk.
S. Labedens; J. L. Scartezzini; D. Mauree : Modeling the effects of future urban planning scenarios on the Urban Heat Island in a complex region. 2018. DOI : 10.31223/osf.io/c8mzb.
F. Guignard; D. Mauree; M. Kanevski; L. Telesca : Wavelet variance scale-dependence as a dynamics discriminating tool in high-frequency urban wind speed time series. 2018.
E. Honeck; R. Castello; B. Chatenoux; J.-P. Richard; A. Lehmann et al. : From a Vegetation Index to a Sustainable Development Goal Indicator: Forest Trend Monitoring Using Three Decades of Earth Observations across Switzerland; ISPRS International Journal of Geo-Information. 2018-11-24. DOI : 10.3390/ijgi7120455.
D. Assouline; N. Mohajeri; J. Scartezzini : Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests; APPLIED ENERGY. 2018. DOI : 10.1016/j.apenergy.2018.02.118.
D. Mauree; N. Blond; A. Clappier : Multi-scale modeling of the urban meteorology: Integration of a new canopy model in the WRF model; Urban Climate. 2018-08-24. DOI : 10.1016/j.uclim.2018.08.002.
T. Q. Nguyen; D. Weitekamp; D. Anderson; R. Castello; O. Cerri et al. : Topology classification with deep learning to improve real-time event selection at the LHC. 2018.
D. Mauree; S. Coccolo; A. Perera; V. Nik; J.-L. Scartezzini et al. : A New Framework to Evaluate Urban Design Using Urban Microclimatic Modeling in Future Climatic Conditions; Sustainability. 2018-04-10. DOI : 10.3390/su10041134.
A. Perera; S. Coccolo; J.-L. Scartezzini; D. Mauree : Quantifying the impact of urban climate by extending the boundaries of urban energy system modeling; Applied Energy. 2018-04-24. DOI : 10.1016/j.apenergy.2018.04.004.
S. Coccolo; J. H. Kämpf; D. Mauree; J.-L. Scartezzini : Cooling potential of greening in the urban environment, a step further towards practice; Sustainable Cities and Society. 2018-01-31. DOI : 10.1016/j.scs.2018.01.019.
N. Mohajeri; D. Assouline; B. Guiboud; A. Bill; A. Gudmundsson et al. : A city-scale roof shape classification using machine learning for solar energy applications; Renewable Energy. 2018. DOI : 10.1016/j.renene.2017.12.096.
M. Le Guen; L. Mosca; A. T. D. Perera; S. Coccolo; N. Mohajeri et al. : Improving the energy sustainability of a Swiss village through building renovation and renewable energy integration; Energy and Buildings. 2018. DOI : 10.1016/j.enbuild.2017.10.057.
T. Chatelain; A. Perera; J.-L. Scartezzini; D. Mauree : Optimum dispatch of a multi-storage and multi-energy hub with demand response and restricted grid interactions; Energy Procedia. 2017-12-01. DOI : 10.1016/j.egypro.2017.12.434.
D. Mauree; S. Coccolo; J. Kaempf; J.-L. Scartezzini : Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale; PLoS ONE. 2017. DOI : 10.1371/journal.pone.0183437.
A. T. D. Perera; V. M. Nik; D. Mauree; J.-L. Scartezzini : An integrated approach to design site specific distributed electrical hubs combining optimization, multi-criterion assessment and decision making; Energy. 2017. DOI : 10.1016/j.energy.2017.06.002.
A. T. D. Perera; V. M. Nik; D. Mauree; J.-L. Scartezzini : Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid; Applied Energy. 2017. DOI : 10.1016/j.apenergy.2016.12.127.
D. Mauree; N. Blond; M. Kohler; A. Clappier : On the Coherence in the Boundary Layer: Development of a Canopy Interface Model; Frontiers in Earth Science. 2017. DOI : 10.3389/feart.2016.00109.
D. Assouline; N. Mohajeri; J.-L. Scartezzini : Quantifying rooftop photovoltaic solar energy potential: A machine learning approach; Solar Energy. 2017. DOI : 10.1016/j.solener.2016.11.045.
N. Mohajeri; G. Upadhyay; A. Gudmundsson; D. Assouline; J. Kämpf et al. : Effects of urban compactness on solar energy potential; Renewable Energy. 2016. DOI : 10.1016/j.renene.2016.02.053.
N. Mohajeri; A. Gudmundsson; J.-L. Scartezzini : Statistical-thermodynamics modelling of the built environment in relation to urban ecology; Ecological Modelling. 2015. DOI : 10.1016/j.ecolmodel.2015.03.014.
N. Mohajeri; A. Gudmundsson; J. R. French : CO2 emissions in relation to street-network configuration and city size; Transportation Research Part D: Transport and Environment. 2015. DOI : 10.1016/j.trd.2014.11.025.
N. Mohajeri; P. Poursistani; P. Poursistani; A. Gudmundsson : Quantitative analysis of structural changes during rapid urban growth; Journal of Urban Planning and Development. 2015. DOI : 10.1061/(ASCE)UP.1943-5444.0000213.
A. Gudmundsson; N. Lecoeur; N. Mohajeri; T. Thordarson : Dike emplacement at Bardarbunga, Iceland, induces unusual stress changes, caldera deformation, and earthquakes; Bulletin Of Volcanology. 2014. DOI : 10.1007/s00445-014-0869-8.
N. Mohajeri; A. Gudmundsson : Analyzing Variation in Street Patterns: Implications for Urban Planning; Journal of Architectural and Planning Research. 2014.
N. Mohajeri; A. Gudmundsson : The Evolution and Complexity of Urban Street Networks; Geographical Analysis. 2014. DOI : 10.1111/gean.12061.
N. Mohajeri; A. Gudmundsson : Quantifying the Differences in Geometry and Size Distributions of Buildings Within Cities; Nexus Network Journal: architecture and mathematics. 2014. DOI : 10.1007/s00004-014-0191-y.
N. Mohajeri; A. Gudmundsson : Street networks in relation to landforms: Implications for fast-growing cities; Journal of Geographical Sciences. 2014. DOI : 10.1007/s11442-014-1093-3.
A. Gudmundsson; N. Mohajeri : Entropy and order in urban street networks; Nature Scientific Reports. 2013. DOI : 10.1038/srep03324.
G. Upadhyay; D. Mauree; J. H. Kämpf; J.-L. Scartezzini : A multi-layer ground model to simulate outdoor surface temperature at urban scale; Energy and Buildings.

Publications under Vahid Nik
Publications under Jérôme Kaempf
Publications under Darren Robinson