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All Journal Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Hafizh, Surya
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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High-Resolution Rooftop Solar PV Potential Assessment Using an Open-Source Remote Sensing Data and Cloud Computing: A Case Study in Padang Utara Subdistrict Hafizh, Surya; Prarikeslan, Widya
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 19 No. 2 (2025)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/inderaja.v19i2.8800

Abstract

High-resolution assessment of rooftop solar photovoltaic (PV) potential in urban areas is often constrained by the high cost of commercial data like LiDAR and the computational intensity of analyzing complex geometries. This study develops and applies a novel, fully open-source remote sensing workflow that leverages cloud computing to overcome these limitations. The methodology integrates open-source building and canopy height data to generate a Digital Surface Model (DSM) and introduces a novel Urban Geometric Correction Factor (UGCF). The UGCF combines a multi-temporal Shading Factor, calculated efficiently in Google Earth Engine (GEE), with a Sky View Factor (SVF) to realistically model solar irradiance on individual rooftops. Applied to the complex urban morphology of Padang Utara, Indonesia, the workflow identified significant potential, with 47.17% of viable rooftops classified as 'Optimal' or 'Very Optimal', with a radiation value range of 758.8–848.63 kWh/m²/year. Spatially, the highest potential is concentrated in lower-profile residential areas, not necessarily on the tallest buildings, Critically revealed that internal roof shading is a dominant limiting factor for large buildings. This research presents a cost-effective and replicable methodology, contributing a significant tool for detailed urban solar potential assessment and supporting data-driven sustainable energy planning.
Co-Authors Widya Prarikeslan