Dan Assouline

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dan.assouline@epfl.ch

Citizenship: French

EPFL > VPA-AVP-CP > SDSC > SDSC-GE

Web site: Web site: https://www.datascience.ch

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Administrative data

Fields of expertise

- Geographic Information Systems, Applied Machine Learning, Renewable energy potential mapping

Education

Phd

Energy / Machine Learning

EPFL

2014-2019

MSc

Civil and Envrionmental Engineering

UC Berkeley

2012-2013

MSc

Civil Engineering

Ecole Sp�ciale des Travaux Publics (ESTP)

2010-2013

BSc

Applied Mathematics

Lyc�e Saint Louis (Classes Pr�paratoires aux Grandes Ecoles)

2008-2010

Publications

Infoscience 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. DOI : 10.1016/j.scs.2023.104451.

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

D. Assouline; R. Castello; D. Mauree; N. Zwahlen; M. Guido et al.

2020-12-14. International Conference on Applied Energy (ICAE 2020), Virtual Conference, December 1-10, 2020.

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

D. Assouline; N. Mohajeri; D. Mauree; J.-L. Scartezzini

2019. CISBAT 2019 | Climate Resilient Cities – Energy Efficiency & Renewables in the Digital Era, Lausanne, Switzerland, September 4-6, 2019. p. 012036. DOI : 10.1088/1742-6596/1343/1/012036.

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

D. Assouline; N. Mohajeri; A. Gudmundsson; J.-L. Scartezzini

Geothermal Energy. 2019-07-25. DOI : 10.1186/s40517-019-0135-6.

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

D. Assouline

Lausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9376.

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

D. Assouline; N. Mohajeri; A. Gudmundsson; J.-L. Scartezzini

2018-01-01. 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, SPAIN, Jul 22-27, 2018. p. 1144-1147. DOI : 10.1109/IGARSS.2018.8517938.

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Estimation of Large-Scale Solar Rooftop PV Potential for Smart Grid Integration: A Methodological Review

D. Assouline; N. Moharjeri; J.-L. Scartezzini

Sustainable Interdependent Networks; Springer International Publishing, 2018.

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

N. Mohajeri; D. Assouline; B. Guiboud; A. Bill; A. Gudmundsson et al.

Renewable Energy. 2018. DOI : 10.1016/j.renene.2017.12.096.

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

N. Mohajeri; D. Assouline; A. Gudmundsson; J.-L. Scartezzini

2017. CISBAT 2017 International Conference, Lausanne, Switzerland, 6-8 September 2017. p. 697-702. DOI : 10.1016/j.egypro.2017.07.372.

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Quantifying rooftop photovoltaic solar energy potential: A machine learning approach

D. Assouline; N. Mohajeri; J.-L. Scartezzini

Solar Energy. 2017. DOI : 10.1016/j.solener.2016.11.045.

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

N. Mohajeri; D. Assouline; B. Guiboud; J.-L. Scartezzini

2016. Zurich, June 15-17 2016; Sustainable Built Environment (SBE) regional conference, Zurich, Switzerland, June 15-17, 2016. DOI : 10.3218/3774-6_21.

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

N. Mohajeri; A. Gudmundsson; T. Kunckler; G. Upadhyay; D. Assouline et al.

2016. PLEA - 36th International Conference on Passive and Low Energy Architecture. Cities, Buildings, People: Towards Regenerative Environments, Los Angeles, July 11-13, 2016.

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Effects of urban compactness on solar energy potential

N. Mohajeri; G. Upadhyay; A. Gudmundsson; D. Assouline; J. Kämpf et al.

Renewable Energy. 2016. DOI : 10.1016/j.renene.2016.02.053.

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

D. Assouline; N. Mohajeri; J.-L. Scartezzini

2015. CISBAT 2015, EPFL, Lausanne, September 9-11th, 2015. p. 555-560. DOI : 10.5075/epfl-cisbat2015-555-560.

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Neighbourhood morphology and solar irradiance in relation to urban climate

N. Mohajeri; A. Gudmundsson; G. Upadhyay; D. Assouline; J.-L. Scartezzini

2015. 9th International Conference on Urban Climate jointly with 12th Symposium on the Urban Environment, Toulouse France, 20- 24 July 2015.

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Online People Directory

Dan Assouline

Fields of expertise

Education

Publications

Infoscience publications

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

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

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

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

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

Estimation of Large-Scale Solar Rooftop PV Potential for Smart Grid Integration: A Methodological Review

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

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

Quantifying rooftop photovoltaic solar energy potential: A machine learning approach

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

Neighbourhood morphology and solar irradiance in relation to urban climate

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