Publications by Roberto Castello

Real-time estimates of Swiss electricity savings using streamed smart meter data

Roberto Castello, Guillaume Romain Obozinski, Fabian Heymann

The gas crisis of 2022 put pressure on electricity prices in Europe, prompting the Swiss government to launch a national energy-saving campaign. To effectively quantify potential savings and guide timely decision-making, this campaign called for rigorous n ...

2025

Explainable machine learning prediction of edema adverse events in patients treated with tepotinib

Roberto Castello, Federico Amato

Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is st ...

2024

Large-scale evaluation of the suitability of buildings for photovoltaic integration: Case study in Greater Geneva

Gilles Desthieux, Roberto Castello

In the context of a rapid and massive deployment or renewable energy and in particular solar photovoltaic, it is necessary to develop methods and tools to guide this deployment. To this end, this work proposes a multicriteria approach for evaluating the su ...

ELSEVIER SCI LTD2022

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

Jean-Louis Scartezzini, Roberto Castello, Kavan Javanroodi

Several contrasting effects are reported in the existing literature concerning the impact assessment of the COVID19 outbreak on the use of energy in buildings. Following an in-depth literature review, we here propose a GISbased approach, based on pre-pande ...

ELSEVIER2022

Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

Jean-Louis Scartezzini, Alina Ursula Rosa Walch, Roberto Castello, Shasha Jiang, Riccardo Cadei

The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-locali ...

IOP PUBLISHING LTD2021

Quantification of existing rooftop PV hourly generation capacity and validation against measurement data

Jean-Louis Scartezzini, Alina Ursula Rosa Walch, Roberto Castello

The spatio-temporal patterns of the existing rooftop photovoltaic (PV) production can provide valuable insights for the design of effective strategies for PV integration at large scale. In this work, we quantify the hourly production of existing rooftop PV ...

IOP PUBLISHING LTD2021

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

Jean-Louis Scartezzini, Nahid Mohajeri Pour Rayeni, Alina Ursula Rosa Walch, Roberto Castello

The increasing use of ground-source heat pumps (GSHPs) for heating and cooling of buildings raises questions regarding the technical potential of GSHPs and their impact on the temperature in the shallow subsurface. In this paper, we develop a method using ...

IOP PUBLISHING LTD2021

Dimensionality reduction and clustering of time series for anomaly detection in a supermarket heating system

Jean-Louis Scartezzini, Lorenzo Salmina, Roberto Castello

A timely identification of an anomalous functioning of the energy system of an industrial building would increase the efficiency and the resilience of the energy infrastructure, beside reducing the economic wastage. This work has been inspired by the need ...

IOP PUBLISHING LTD2021

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

Jean-Louis Scartezzini, Nahid Mohajeri Pour Rayeni, Alina Ursula Rosa Walch, Roberto Castello

The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to e ...

2020

Spatio-Temporal Relationship between Land Cover and Land Surface Temperature in Urban Areas: A Case Study in Geneva and Paris

Jean-Louis Scartezzini, Dasaraden Mauree, Roberto Castello, Xu Ge

Currently, more than half of the world’s population lives in cities, which leads to major changes in land use and land surface temperature (LST). The associated urban heat island (UHI) effects have multiple impacts on energy consumption and human health. A ...

2020

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

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

2020

Geospatial analysis and optimization of the incoming and stored CO2 emissions within the EPFL campus

Jean-Louis Scartezzini, Silvia Coccolo, Roberto Castello, Giovanni Mori

All the projected scenarios agree on the immediate necessity of reducing greenhouse gas emissions, especially carbon dioxide (CO2). This research proposes an analysis on the EPFL campus of all the potential reductions of CO2 emissions: energy refurbishment ...

IOP science2019

Wind profile prediction in an urban canyon: a machine learning approach

Jean-Louis Scartezzini, Dasaraden Mauree, Tianchu Zhang, Roberto Castello, Gianluca Mancini, Tullio Nutta

Resolving the wind profile in an urban canyon environment means dealing with the turbulent nature of the stream and the presence of non-negligible flux exchanges with the atmosphere inside the canopy, making any deterministic model solution computationally ...

IOP2019

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

Jean-Louis Scartezzini, Fabian Guignard, Nahid Mohajeri Pour Rayeni, Alina Ursula Rosa Walch, Roberto Castello

2019

Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks

Jean-Louis Scartezzini, Adrian Guerra, Martin Esguerra, Roberto Castello

Mapping the location and size of solar installations in urban areas can be a valuable input for policymakers and for investing in distributed energy infrastructures. Machine Learning techniques, combined with satellite and aerial imagery, allow to overcome ...

IOP Science2019

From a Vegetation Index to a Sustainable Development Goal Indicator: Forest Trend Monitoring Using Three Decades of Earth Observations across Switzerland

Roberto Castello

2018

Observation of the rare B-s(0)->mu(+)mu(-) decay from the combined analysis of CMS and LHCb data

Horst Vogel, Christoph Frei, Peter Clarke, Jean Wicht, Alessandro Mapelli, Aurelio Bay, Minh Tâm Tran, Andrea Rizzi, Marc-Olivier Bettler, Cédric Potterat, Matthias Finger, Tatsuya Nakada, Guido Haefeli, Niko Neufeld, Olivier Schneider, Pierre Jaton, Hans Dijkstra, Julien Rouvinet, Charles Dietz, João Miguel das Neves Duarte, Frédéric Guillaume Dupertuis, Frédéric Teubert, Gerhard Raven, Quentin Python, Bastien Luca Muster, Joo Yeon Kim, Alexis Kalogeropoulos, Chitsanu Khurewathanakul, Frédéric Blanc, Matthew Needham, Lesya Shchutska, Liang Sun, Alexandre Aubin, Yixing Chen, Tian Cheng, Ioannis Papadopoulos, Wei Sun, Barinjaka Rakotomiaramanana, Xiaoxue Han, Qian Wang, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,