Francesco Petrosino
Assessment of rooftop solar photovoltaic potential through spatial analysis and implementation of machine learning techniques.
Rel. Andrea Lanzini, Daniele Salvatore Schiera, Riccardo Novo. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Energetica E Nucleare, 2022
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Abstract
The necessity to reduce greenhouse gas emissions to meet the global warming regulations has increased the demand for renewable energy sources; unlike power generation from fossil fuels, renewables have relatively low geographic density and they’re unpredictable. Solar energy deployment is gaining greater attention and it’s a technically and economically feasible solution as a sustainable alternative that might alleviate aspects of the current climate crisis, especially in cities, where it is crucial to promote the use of solar technologies. The roof surfaces within urban areas are constantly attracting interest as they supply huge potentials for the mitigation strategy to minimize the environmental impact by achieving the sustainable development goals.
The local generation of renewable electricity through roof-mounted photovoltaic (PV) systems on buildings in urban areas can play a significant role within the transition to a low-carbon energy system as the resulting large-scale deployment is quite straightforward once the methodology of the solar energy potential assessment has been developed
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