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SCALABLE METHODOLOGY FOR SOLAR POTENTIAL OF ROOFTOPS AND BUILDING ENVIRONMENT BASED ON SHADOW ANALYSIS

Bugrahan Coban

SCALABLE METHODOLOGY FOR SOLAR POTENTIAL OF ROOFTOPS AND BUILDING ENVIRONMENT BASED ON SHADOW ANALYSIS.

Rel. Pierluigi Leone, Romano Borchiellini, Andrea Lanzini. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering), 2022

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Abstract:

In the changing world with ongoing increase in energy demand and relatively the negative effect on the environment through fossil fuels has raised dramatical challenges. Renewable energy sources are increasingly considered as potential solutions for a sustainable energy production and reduction of negative environmental impact is aimed. In Italy, the need to decrease the country’s historical dependence on fossil fuels and supply flows of hydrocarbons from Eastern Europe, Middle East and North Africa has caused an irresistible urge to produce green energy. Therefore, in the twentieth century, Italy has become one of the leading countries in the development of green energy production. Solar energy is one of the major unlimited and clean renewable energy sources that is gradually replacing non-renewable energy sources. This thesis adopts a mixed methodology with an interdisciplinary framework in order to improve the current methodologies of the estimation for solar photovoltaics potentials. Unfortunately, some roof surfaces are unsuitable for PV systems’ installation mainly because of shadowing effect. Therefore, the shadowing effect is considered, and four-days-shading method is applied to detect and extract the shadowed unsuitable areas of rooftops. Later the photovoltaic technical potential is evaluated based on the Solar Energy on Building Envelopes (SEBE) model, which estimates shortwave irradiance on ground and roofs that is the main input data for the subsequent calculations. The methodology is applied on two different spatial resolutions of raster that are respectively 2m and 0.5m. The proposed methodology is implemented on geographic information system (GIS) software and Python programming language to perform the geospatial analysis of the unsuitable areas and visualize the generated resource maps of Aosta Valley.

Relatori: Pierluigi Leone, Romano Borchiellini, Andrea Lanzini
Anno accademico: 2022/23
Tipo di pubblicazione: Elettronica
Numero di pagine: 105
Soggetti:
Corso di laurea: Corso di laurea magistrale in Ingegneria Meccanica (Mechanical Engineering)
Classe di laurea: Nuovo ordinamento > Laurea magistrale > LM-33 - INGEGNERIA MECCANICA
Aziende collaboratrici: NON SPECIFICATO
URI: http://webthesis.biblio.polito.it/id/eprint/24393
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