IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i23p7760-d1287299.html
   My bibliography  Save this article

Trends and Evolution of the GIS-Based Photovoltaic Potential Calculation

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/23/7760/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/23/7760/
    Download Restriction: no
    ---><---

    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. 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).
    4. 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).
    5. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Tianxi & Zhang, Yi & Zhang, Yuhang & Miao, Rui & Kang, Jian & Qi, He, 2024. "City-scale roof-top photovoltaic deployment planning," Applied Energy, Elsevier, vol. 368(C).
    2. Jiang, Hou & Yao, Ling & Lu, Ning & Qin, Jun & Zhang, Xiaotong & Liu, Tang & Zhang, Xingxing & Zhou, Chenghu, 2024. "Exploring the optimization of rooftop photovoltaic scale and spatial layout under curtailment constraints," Energy, Elsevier, vol. 293(C).
    3. D'Adamo, Idiano & Gastaldi, Massimo & Morone, Piergiuseppe & Ozturk, Ilhan, 2022. "Economics and policy implications of residential photovoltaic systems in Italy's developed market," Utilities Policy, Elsevier, vol. 79(C).
    4. Ren, Haoshan & Ma, Zhenjun & Fai Norman Tse, Chung & Sun, Yongjun, 2022. "Optimal control of solar-powered electric bus networks with improved renewable energy on-site consumption and reduced grid dependence," Applied Energy, Elsevier, vol. 323(C).
    5. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    6. Zhang, Kai & Wang, Dajiang & Chen, Min & Zhu, Rui & Zhang, Fan & Zhong, Teng & Qian, Zhen & Wang, Yazhou & Li, Hengyue & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2024. "Power generation assessment of photovoltaic noise barriers across 52 major Chinese cities," Applied Energy, Elsevier, vol. 361(C).
    7. Hasheminasab, Hamidreza & Streimikiene, Dalia & Pishahang, Mohammad, 2023. "A novel energy poverty evaluation: Study of the European Union countries," Energy, Elsevier, vol. 264(C).
    8. Özdemir, Samed & Yavuzdoğan, Ahmet & Bilgilioğlu, Burhan Baha & Akbulut, Zeynep, 2023. "SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data," Renewable Energy, Elsevier, vol. 216(C).
    9. Shao, Shuai & Tan, Zhijia & Liu, Zhiyuan & Shang, Wenlong, 2022. "Balancing the GHG emissions and operational costs for a mixed fleet of electric buses and diesel buses," Applied Energy, Elsevier, vol. 328(C).
    10. Liao, Xuan & Zhu, Rui & Wong, Man Sing & Heo, Joon & Chan, P.W. & Kwok, Coco Yin Tung, 2023. "Fast and accurate estimation of solar irradiation on building rooftops in Hong Kong: A machine learning-based parameterization approach," Renewable Energy, Elsevier, vol. 216(C).
    11. Li, Yanhao & Li, Xin & Zhang, Chengdong & Zhang, Yanxi, 2024. "Optimizing the photovoltaic-assisted electric bus network with rooftop energy supply," Renewable Energy, Elsevier, vol. 234(C).
    12. Li, Qingyu & Krapf, Sebastian & Mou, Lichao & Shi, Yilei & Zhu, Xiao Xiang, 2024. "Deep learning-based framework for city-scale rooftop solar potential estimation by considering roof superstructures," Applied Energy, Elsevier, vol. 374(C).
    13. Murugan, Manivel & Marisamynathan, Sankaran, 2024. "Policy analysis for sustainable EV charging facility adoption using SEM-ANN approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    14. Maria Krechowicz & Adam Krechowicz & Lech Lichołai & Artur Pawelec & Jerzy Zbigniew Piotrowski & Anna Stępień, 2022. "Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning," Energies, MDPI, vol. 15(11), pages 1-21, May.
    15. Kılkış, Şiir, 2023. "Integrated urban scenarios of emissions, land use efficiency and benchmarking for climate neutrality and sustainability," Energy, Elsevier, vol. 285(C).
    16. Tianyi Chen & Yaning An & Chye Kiang Heng, 2022. "A Review of Building-Integrated Photovoltaics in Singapore: Status, Barriers, and Prospects," Sustainability, MDPI, vol. 14(16), pages 1-25, August.
    17. Izabela Jonek-Kowalska & Wieslaw Grebski, 2024. "Autarky and the Promotion of Photovoltaics for Sustainable Energy Development: Prosumer Attitudes and Choices," Energies, MDPI, vol. 17(16), pages 1-27, August.
    18. Ren, Haoshan & Sun, Yongjun & Norman Tse, Chung Fai & Fan, Cheng, 2023. "Optimal packing and planning for large-scale distributed rooftop photovoltaic systems under complex shading effects and rooftop availabilities," Energy, Elsevier, vol. 274(C).
    19. Liu, Xiaohan & Yeh, Sonia & Plötz, Patrick & Ma, Wenxi & Li, Feng & Ma, Xiaolei, 2024. "Electric bus charging scheduling problem considering charging infrastructure integrated with solar photovoltaic and energy storage systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 187(C).
    20. Zhang, Jinlai & Yang, Wenjie & Chen, Yumei & Ding, Mingkang & Huang, Huiling & Wang, Bingkun & Gao, Kai & Chen, Shuhan & Du, Ronghua, 2024. "Fast object detection of anomaly photovoltaic (PV) cells using deep neural networks," Applied Energy, Elsevier, vol. 372(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7760-:d:1287299. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.