Abstract
The last guidelines approved by Italian government to financially support the solar Photovoltaic (PV) Energy production development (Fourth and Fifth feed-in-scheme, January 2012 and later), in order to avoid soil consumption in agricultural or naturals areas, include specific indications for more advantageously funding installations exploiting roofs or covers surfaces. In this context it becomes important, for a suitable PV planning and monitoring, the extensive mapping of the available surfaces extent, usually corresponding to covers and properly assessing their quality in term of PV potential. Since the covers are mainly located in urban or industrial areas, whose 3D heterogeneity, albedo, atmospheric turbidity and casting shadows significantly influence the local solar irradiance, it is necessary to suitably account for these distributed factors by means of GIS mapping and advanced modeling tools in order to provide realistic estimates of solar available radiance at roofs level. The implemented methodology, based on remote sensing techniques, has allowed to estimate and map the global solar radiance over all the roofs within Avellino (southern Italy) municipality. Starting from LIDAR data, DSM of the entire area of interest (~42 Km2) has been firstly obtained; then the 3D model of each building and related cover has been derived. To account the atmospheric transparency and the related time-dependent diffuse/direct radiation percentage on the area, data and tools from EU PVGIS web application have been also used. The final processing to obtain the solar radiance maps has been carried out using specific software modules available within commercial and open-source GIS packages.
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Borfecchia, F. et al. (2013). Integrated GIS and Remote Sensing Techniques to Support PV Potential Assessment of Roofs in Urban Areas. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39646-5_31
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DOI: https://doi.org/10.1007/978-3-642-39646-5_31
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