Publications by Nahid Mohajeri Pour Rayeni

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

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

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

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

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

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

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

Quantifying the technical geothermal potential from shallow borehole heat exchangers at regional scale

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

The extraction of shallow geothermal energy using borehole heat exchangers (BHEs) is a promising approach for decarbonisation of the heating sector. However, a dense deployment of BHEs may lead to thermal interference between neighboring boreholes and ther ...

Pergamon-Elsevier Science Ltd2020

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

A critical comparison of methods to estimate solar rooftop photovoltaic potential in Switzerland

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

To understand the opportunities and challenges of large shares of solar photovoltaics (PV) in our electricity mix, various large-scale studies of PV potential on building roofs have been conducted in recent years. The use of different datasets, methods and ...

2019

Integrating urban form and distributed energy systems: Assessment of sustainable development scenarios for a Swiss village to 2050

Jean-Louis Scartezzini, Silvia Coccolo, Amarasinghage Tharindu Dasun Perera, Nahid Mohajeri Pour Rayeni, Lucas Mosca, Morgane Le Guen

The integration of renewable energy in the built environment, improving the energy efficiency of buildings, and optimizing distributed energy systems are three interconnected activities that are central for the transition to sustainable cities. Here, a met ...

Pergamon-Elsevier Science Ltd2019

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

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

2019

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