Fields of expertise
- Geographic Information Systems, Applied Machine Learning, Renewable energy potential mapping
Energy / Machine Learning
Civil and Envrionmental Engineering
Ecole Sp�ciale des Travaux Publics (ESTP)
Lyc�e Saint Louis (Classes Pr�paratoires aux Grandes Ecoles)
A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United KingdomSustainable Cities And Society. 2023-02-22. DOI : 10.1016/j.scs.2023.104451.
A machine learning-assisted building electricity consumption profiling for anomaly detection2020-12-14. International Conference on Applied Energy (ICAE 2020), Virtual Conference, December 1-10, 2020.
A machine learning approach for mapping the very shallow theoretical geothermal potentialGeothermal Energy. 2019-07-25. DOI : 10.1186/s40517-019-0135-6.
Machine learning and geographic information systems for large-scale wind energy potential estimation in rural areas2019. 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.
Machine Learning and Geographic Information Systems for large-scale mapping of renewable energy potentialLausanne, EPFL, 2019. DOI : 10.5075/epfl-thesis-9376.
Combining Fourier Analysis And Machine Learning To Estimate The Shallow-Ground Thermal Diffusivity In Switzerland2018-01-01. 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, SPAIN, Jul 22-27, 2018. p. 1144-1147. DOI : 10.1109/IGARSS.2018.8517938.
Estimation of Large-Scale Solar Rooftop PV Potential for Smart Grid Integration: A Methodological ReviewSustainable Interdependent Networks; Springer International Publishing, 2018.
A city-scale roof shape classification using machine learning for solar energy applicationsRenewable Energy. 2018. DOI : 10.1016/j.renene.2017.12.096.
Effects of city size on the large-scale decentralised solar energy potential2017. CISBAT 2017 International Conference, Lausanne, Switzerland, 6-8 September 2017. p. 697-702. DOI : 10.1016/j.egypro.2017.07.372.
Quantifying rooftop photovoltaic solar energy potential: A machine learning approachSolar Energy. 2017. DOI : 10.1016/j.solener.2016.11.045.
Does roof shape matter? Solar photovoltaic (PV) integration on building roofs2016. Zurich, June 15-17 2016; Sustainable Built Environment (SBE) regional conference, Zurich, Switzerland, June 15-17, 2016. DOI : 10.3218/3774-6_21.
How street canyon configuration control the accessibility of solar energy potential: Implication for urban design2016. PLEA - 36th International Conference on Passive and Low Energy Architecture. Cities, Buildings, People: Towards Regenerative Environments, Los Angeles, July 11-13, 2016.
Effects of urban compactness on solar energy potentialRenewable Energy. 2016. DOI : 10.1016/j.renene.2016.02.053.
A machine learning methodology for estimating roof-top photovoltaic solar energy potential in Switzerland2015. CISBAT 2015, EPFL, Lausanne, September 9-11th, 2015. p. 555-560. DOI : 10.5075/epfl-cisbat2015-555-560.
Neighbourhood morphology and solar irradiance in relation to urban climate2015. 9th International Conference on Urban Climate jointly with 12th Symposium on the Urban Environment, Toulouse France, 20- 24 July 2015.