Abstract
The world faces the threat of an energy crisis that is exacerbated by the dominance of fossil energy sources that negatively impact the sustainability of the earth’s ecosystem. Currently, efforts to increase the supply of renewable energy have become a global agenda, including using solar energy which is one of the rapidly developing clean energies. However, studies in solar photovoltaic (PV) modelling that integrates geospatial information of urban morphological building characters, solar radiation, and multiple meteorological parameters in low-cost scope have not been explored fully. Therefore, this research aims to model the urban rooftop solar PV development in the Global South using Bandung, Indonesia, as a case study. This research also has several specific purposes: developing a building height model as well as determining the energy potential of rooftop solar PV, the energy needs of each building, and the residential property index. This study is among the first to develop the national digital surface model (DSM) of buildings. In addition, the analysis of meteorological effects integrated with the hillshade parameter was used to obtain the solar PV potential value of the roof in more detail. The process of integrating building parameters in the form of rooftop solar PV development potential, energy requirements, and residential property index of a building was expected to increase the accuracy of determining priority buildings for rooftop solar PV deployment in Bandung. This study shows that the estimated results of effective solar PV in Bandung ranges from 351.833 to 493.813 W/m2, with a total of 1316 and 36,372 buildings in scenarios 1 and 2 being at a high level of priority for solar PV development. This study is expected to be a reference for the Indonesian government in planning the construction of large-scale rooftop solar PV in urban areas to encourage the rapid use of clean energy. Furthermore, this study has general potential for other jurisdictions for the governments focusing on clean energy using geospatial information in relation with buildings and their energy consumption.
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