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Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation

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  • Sebastiano Anselmo

    (Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10125 Turin, Italy)

  • Maria Ferrara

    (Department of Energy, Politecnico di Torino, 10129 Turin, Italy)

Abstract

In the current framework of energy transition, renewable energy production has gained a renewed relevance. A set of 75 papers was selected from the existing literature and critically analyzed to understand the main inputs and tools used to calculate solar energy and derive theoretical photovoltaic production based on geographic information systems (GISs). A heterogeneous scenario for solar energy estimation emerged from the analysis, with a prevalence of 2.5D tools—mainly ArcGIS and QGIS—whose calculation is refined chiefly by inputting weather data from databases. On the other hand, despite some minor changes, the formula for calculating the photovoltaic potential is widely acknowledged and includes solar energy, exploitable surface, performance ratio, and panel efficiency. While sectorial studies—targeting a specific component of the calculation—are sound, the comprehensive ones are generally problematic due to excessive simplification of some parts. Moreover, validation is often lacking or, when present, only partial. The research on the topic is in constant evolution, increasingly moving towards purely 3D models and refining the estimation to include the time component—both in terms of life cycle and variations between days and seasons.

Suggested Citation

  • Sebastiano Anselmo & Maria Ferrara, 2023. "Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation," Energies, MDPI, vol. 16(23), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7760-:d:1287299
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    References listed on IDEAS

    as
    1. Prades-Gil, C. & Viana-Fons, J.D. & Masip, X. & Cazorla-Marín, A. & Gómez-Navarro, T., 2023. "An agile heating and cooling energy demand model for residential buildings. Case study in a mediterranean city residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    2. Ren, Haoshan & Xu, Chengliang & Ma, Zhenjun & Sun, Yongjun, 2022. "A novel 3D-geographic information system and deep learning integrated approach for high-accuracy building rooftop solar energy potential characterization of high-density cities," Applied Energy, Elsevier, vol. 306(PA).
    3. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    4. Zhu, Rui & Cheng, Cheng & Santi, Paolo & Chen, Min & Zhang, Xiaohu & Mazzarello, Martina & Wong, Man Sing & Ratti, Carlo, 2022. "Optimization of photovoltaic provision in a three-dimensional city using real-time electricity demand," Applied Energy, Elsevier, vol. 316(C).
    5. Ren, Haoshan & Ma, Zhenjun & Ming Lun Fong, Alan & Sun, Yongjun, 2022. "Optimal deployment of distributed rooftop photovoltaic systems and batteries for achieving net-zero energy of electric bus transportation in high-density cities," Applied Energy, Elsevier, vol. 319(C).
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