Publications de Dan Assouline | EPFL Graph Search

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

Regional-scale urban residential densification provides an opportunity to tackle multiple challenges of sustain-ability in cities. But framework for detailed large-scale analysis of densification potentials and their integration with natural capital to ass ...

ELSEVIER2023

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

Dan Assouline, Alina Ursula Rosa Walch, Nahid Mohajeri Pour Rayeni

Using Machine Learning (ML) algorithms for classification of the existing residential neighbourhoods and their spatial characteristics (e.g. density) so as to provide plausible scenarios for designing future sustainable housing is a novel application. Here ...

IOP PUBLISHING LTD2021

A machine learning-assisted building electricity consumption profiling for anomaly detection

Roberto Castello, Jean-Louis Scartezzini, Dasaraden Mauree, Dan Assouline, Michele Vidulis, Daniele Hamm, Margherita Guido, Nicolas Zwahlen

2020

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

Dan Assouline

A promising pathway to follow in order to reach sustainable development goals is an increased reliance on renewable sources of energy. The optimized use of these energy sources, however, requires the assessment of their potential supply, along with the dem ...

EPFL2019

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

Jean-Louis Scartezzini, Dan Assouline, Nahid Mohajeri Pour Rayeni

The very shallow geothermal potential (vSGP) is increasingly recognized as a viable resource for providing clean thermal energy in urban and rural areas. This is primarily due to its reliability, low-cost installation, easy maintenance, and little constrai ...

2019

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

Jean-Louis Scartezzini, Dan Assouline, Jérôme Henri Kämpf, Nahid Mohajeri Pour Rayeni

Buildings commonly have the largest share in the energy demand of a country, but they also offer sites for the generation of solar energy. Here we develop a methodology to analyse the effects of street-canyon geometries on the solar access of street surfac ...

2019

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

Jean-Louis Scartezzini, Dasaraden Mauree, Dan Assouline, Nahid Mohajeri Pour Rayeni

Clean, safe, affordable and available in the long-term, wind is one of the most promising sources of renewable energy. Its optimized and profitable use, however, requires an estimation of the potential in locations of interest, given its very volatile beha ...

IOP2019

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

Jean-Louis Scartezzini, Dan Assouline, Nahid Mohajeri Pour Rayeni

2018

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

Jean-Louis Scartezzini, Dan Assouline, Nahid Mohajeri Pour Rayeni, Bérénice Guiboud, Andreas Bill

Solar energy deployment through PV installations in urban areas depends strongly on the shape, size, and orientation of available roofs. Here we use a machine learning approach, Support Vector Machine (SVM) classification, to classify 10,085 building roofs ...

Pergamon-Elsevier Science Ltd2018

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

Jean-Louis Scartezzini, Dan Assouline, Nahid Mohajeri Pour Rayeni

We propose a methodology combining physical modelling and machine learning (ML) to estimate the apparent ground thermal diffusivity at the scale of a country. Based on ground temperature time series at different depths, we estimate the diffusivity at 49 Sw ...

IEEE2018