26 October 2016 Assessing the urban solar energy resource potential of Davao City, Philippines, using LiDAR Digital Surface Model (DSM) and GRASS GIS
Author Affiliations +
Solar energy is emerging as one of the top options for renewable energy sources in the Philippines, with largescale solar photovoltaic (PV) farms being built all over the country. Solar energy resource in the urban environment has great potential in making a city self-sustaining, but has not been fully explored for the country. In order to represent its potential, reliable resource assessment should be done. This study aims to assess the available solar energy resource in Davao City, a trade and commerce hub in southern Philippines. The functions of GRASS GIS, specifically the r.sun module, in modelling incoming solar radiation is discussed, along with the use of a one-meter LiDAR Digital Surface Model (DSM) and Linke Turbidity coefficients as inputs. The average Julian day of each month was used to compute the Global Horizontal Irradiation (GHI) values under clear-sky or cloudless conditions. To account for the effects of the clouds in the study area, the clear-sky indices (Kc) were computed using data from solar recording stations of the Bureau of Soils and Water Management (BSWM) found within and around the region. These were multiplied to the modelled clear-sky GHI rasters to get the real-sky GHI. The results show that the city’s average GHI potential ranges from 2693.79 Wh/m2 and 4453.13 Wh/m2. Average values are particularly higher around the months of March and April, while lower values are seen in the months of November and January. Areas with higher potential are seen in the southern portion of the city, consistent in built-up areas.
Conference Presentation
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Justine Teves, Justine Teves, Eula Fae Sola, Eula Fae Sola, Ben Hur Pintor, Ben Hur Pintor, Ma. Rosario Concepcion Ang, Ma. Rosario Concepcion Ang, } "Assessing the urban solar energy resource potential of Davao City, Philippines, using LiDAR Digital Surface Model (DSM) and GRASS GIS", Proc. SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments, 1000809 (26 October 2016); doi: 10.1117/12.2241407; https://doi.org/10.1117/12.2241407

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