Solar Photovoltaics (PV), Renewable Energy Transition, Convolutional Neural Network, Solar PV adoption, Urban From
Abstract :
[en] Residential Solar Photovoltaics (RSPV) offer a promising path towards partial energy self-sufficiency and enhanced national energy security by allowing households to inject surplus energy back into the grid. However, the challenges of RSPV implementation in urban environments, influenced by urban form, need to be better understood and addressed. Using a machine learning approach combined with spatial regression analysis this search aims to investigate the interplay between urban spatial configurations and the existing patterns of rooftop PV installations in Luxembourg. By examining building layouts, environmental conditions, and architectural diversity, we identify gaps in RSPV
implementation and understanding of their relationship with urban form.
Disciplines :
Human geography & demography
Author, co-author :
SKINNER, Alexander ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Geography and Spatial Planning (DGEO) > Geography and Spatial Planning
JONES, Catherine ; University of Luxembourg > Faculty of Humanities, Education and Social Sciences (FHSE) > Department of Geography and Spatial Planning (DGEO) > Geography and Spatial Planning ; Unilu - University of Luxembourg [LU] > Institute of Advanced Studies
Language :
English
Title :
Exploring the Spatial and Built Environmental Characteristics of Residential Solar Photovoltaic Implementations in Luxembourg
