IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v259y2026ics0960148125026898.html

Deep learning and explainable Artificial Intelligence for large-scale photovoltaic suitability analysis in Australian cities

Author

Listed:
  • Shen, Jie
  • Zheng, Fanghao
  • Tesema, Fiseha Berhanu
  • Deng, Wu
  • Bellotti, Anthony Graham
  • Xie, Jing

Abstract

To support the achievement of the United Nations Sustainable Development Goals (SDGs), the global energy sector is undergoing a significant transformation towards renewable sources, with solar PV technology at the forefront. In 2023, global installed PV capacity surpassed 1.6 TW (TW), reflecting an accelerating trend in renewable energy adoption. Consequently, selecting suitable sites for PV development and analyzing PV suitability have become increasingly critical. A large body of research focuses on this area, reflecting the varying priorities of different countries or regions regarding PV suitability. The rapid expansion of PV in Australia provides an opportunity to reevaluate whether solar radiation should be considered the primary factor. This study investigates the key drivers of PV suitability in urban areas by assuming that existing PV installations reflect realistic locational conditions. High-resolution spatial datasets incorporating geographic, environmental, social, and urban planning variables were integrated with several deep learning models, U-Net, ResNet, CNN + Transformer, FCN, and SimpleCNN, as well as the AHP for comparative analysis. Results indicate that Res-Net achieves the highest accuracy in predicting PV distribution. XAI analysis further reveals that the top 2 influential factors across cities are Population Distribution (8.96 %) and Forest Coverage (8.09 %), while GHI ranks only third (7.48 %). These findings uncover the intrinsic logic within the actual PV distribution and suggest that in urban environments of Australia, social and infrastructural variables may outweigh pure solar-related indicators. This study offers new insights for optimizing PV planning strategies in diverse global contexts.

Suggested Citation

  • Shen, Jie & Zheng, Fanghao & Tesema, Fiseha Berhanu & Deng, Wu & Bellotti, Anthony Graham & Xie, Jing, 2026. "Deep learning and explainable Artificial Intelligence for large-scale photovoltaic suitability analysis in Australian cities," Renewable Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:renene:v:259:y:2026:i:c:s0960148125026898
    DOI: 10.1016/j.renene.2025.125025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125026898
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.125025?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Sun, Yanwei & Li, Ying & Wang, Run & Ma, Renfeng, 2022. "Measuring dynamics of solar energy resource quality: Methodology and policy implications for reducing regional energy inequality," Renewable Energy, Elsevier, vol. 197(C), pages 138-150.
    2. Salah Ghabri & Jean-Michel Josselin & Benoît Le Maux, 2019. "Could or Should We Use MCDA in the French HTA Process?," Post-Print halshs-02319704, HAL.
    3. Al Garni, Hassan Z. & Awasthi, Anjali, 2017. "Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia," Applied Energy, Elsevier, vol. 206(C), pages 1225-1240.
    4. Ferretti, Valentina & Geneletti, Davide, 2020. "Does the spatial representation affect criteria weights in environmental decision-making? Insights from a behavioral experiment," Land Use Policy, Elsevier, vol. 97(C).
    5. Fotsing Metegam, Isabelle Flora & Wolff, Eleonore & Tchinda, René & Huart, Michel & Chara-Dackou, Venant Sorel, 2025. "Evaluation of on-grid and off-grid solar photovoltaic sites in Cameroon using geographic information systems, fuzzy logic, and multi-criteria analysis," Energy, Elsevier, vol. 318(C).
    6. Jangwon Suh & Jeffrey R. S. Brownson, 2016. "Solar Farm Suitability Using Geographic Information System Fuzzy Sets and Analytic Hierarchy Processes: Case Study of Ulleung Island, Korea," Energies, MDPI, vol. 9(8), pages 1-24, August.
    7. Tao, Linwei & Hayashi, Kiichiro & Gyeltshen, Sangay & Shimoyama, Yuya, 2025. "Spatial assessment of utility-scale solar photovoltaic siting potential using machine learning approaches: A case study in Aichi prefecture, Japan," Applied Energy, Elsevier, vol. 383(C).
    8. Majumdar, Debaleena & Pasqualetti, Martin J., 2019. "Analysis of land availability for utility-scale power plants and assessment of solar photovoltaic development in the state of Arizona, USA," Renewable Energy, Elsevier, vol. 134(C), pages 1213-1231.
    9. Salah Ghabri & Jean-Michel Josselin & Benoît Maux, 2019. "Could or Should We Use MCDA in the French HTA Process?," PharmacoEconomics, Springer, vol. 37(12), pages 1417-1419, December.
    10. Brewer, Justin & Ames, Daniel P. & Solan, David & Lee, Randy & Carlisle, Juliet, 2015. "Using GIS analytics and social preference data to evaluate utility-scale solar power site suitability," Renewable Energy, Elsevier, vol. 81(C), pages 825-836.
    11. Sánchez-Lozano, Juan M. & Teruel-Solano, Jerónimo & Soto-Elvira, Pedro L. & Socorro García-Cascales, M., 2013. "Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 544-556.
    12. Uyan, Mevlut, 2013. "GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 11-17.
    13. Dimitriou, Iason C. & Arsenopoulos, Apostolos & Trachanas, Georgios P. & Sarmas, Elissaios & Marinakis, Vangelis, 2024. "Assessing the adoption level of solar PV installations, on district scale of urban environment," Renewable Energy, Elsevier, vol. 237(PB).
    14. Noorollahi, Younes & Ghenaatpisheh Senani, Ali & Fadaei, Ahmad & Simaee, Mobina & Moltames, Rahim, 2022. "A framework for GIS-based site selection and technical potential evaluation of PV solar farm using Fuzzy-Boolean logic and AHP multi-criteria decision-making approach," Renewable Energy, Elsevier, vol. 186(C), pages 89-104.
    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. Finn, Thomas & McKenzie, Paul, 2020. "A high-resolution suitability index for solar farm location in complex landscapes," Renewable Energy, Elsevier, vol. 158(C), pages 520-533.
    2. Fotsing Metegam, Isabelle Flora & Wolff, Eleonore & Tchinda, René & Huart, Michel & Chara-Dackou, Venant Sorel, 2025. "Evaluation of on-grid and off-grid solar photovoltaic sites in Cameroon using geographic information systems, fuzzy logic, and multi-criteria analysis," Energy, Elsevier, vol. 318(C).
    3. Li, Xiao-Ya & Dong, Xin-Yu & Chen, Sha & Ye, Yan-Mei, 2024. "The promising future of developing large-scale PV solar farms in China: A three-stage framework for site selection," Renewable Energy, Elsevier, vol. 220(C).
    4. Kwak, Yoonshin & Deal, Brian & Heavisides, Tom, 2021. "A large scale multi criteria suitability analysis for identifying solar development potential: A decision support approach for the state of Illinois, USA," Renewable Energy, Elsevier, vol. 177(C), pages 554-567.
    5. Sward, Jeffrey A. & Nilson, Roberta S. & Katkar, Venktesh V. & Stedman, Richard C. & Kay, David L. & Ifft, Jennifer E. & Zhang, K. Max, 2021. "Integrating social considerations in multicriteria decision analysis for utility-scale solar photovoltaic siting," Applied Energy, Elsevier, vol. 288(C).
    6. Okazawa, Yuki & Hino, Kimihiro & Asami, Yasushi, 2025. "Photovoltaic power plant installation in the Tokyo metropolitan area, Japan, focusing on land use and regulatory ordinances," Land Use Policy, Elsevier, vol. 152(C).
    7. Ali, Shahid & Taweekun, Juntakan & Techato, Kuaanan & Waewsak, Jompob & Gyawali, Saroj, 2019. "GIS based site suitability assessment for wind and solar farms in Songkhla, Thailand," Renewable Energy, Elsevier, vol. 132(C), pages 1360-1372.
    8. Colak, H. Ebru & Memisoglu, Tugba & Gercek, Yasin, 2020. "Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: A case study of Malatya Province, Turkey," Renewable Energy, Elsevier, vol. 149(C), pages 565-576.
    9. López-Bravo, Celia & Mora-López, Llanos & Sidrach-deCardona, Mariano & Márquez-Ballesteros, María José, 2024. "A comprehensive analysis based on GIS-AHP to minimise the social and environmental impact of the installation of large-scale photovoltaic plants in south Spain," Renewable Energy, Elsevier, vol. 226(C).
    10. Shao, Meng & Han, Zhixin & Sun, Jinwei & Xiao, Chengsi & Zhang, Shulei & Zhao, Yuanxu, 2020. "A review of multi-criteria decision making applications for renewable energy site selection," Renewable Energy, Elsevier, vol. 157(C), pages 377-403.
    11. Sultan Al-Shammari & Wonsuk Ko & Essam A. Al Ammar & Majed A. Alotaibi & Hyeong-Jin Choi, 2021. "Optimal Decision-Making in Photovoltaic System Selection in Saudi Arabia," Energies, MDPI, vol. 14(2), pages 1-18, January.
    12. Majumdar, Debaleena & Pasqualetti, Martin J., 2019. "Analysis of land availability for utility-scale power plants and assessment of solar photovoltaic development in the state of Arizona, USA," Renewable Energy, Elsevier, vol. 134(C), pages 1213-1231.
    13. Al-Abadi, Alaa M. & Handhal, Amna M. & Abdulhasan, Mustafa A. & Ali, Wajdi L. & Hassan, J.J. & Al Aboodi, Ali H., 2025. "Optimal siting of large photovoltaic solar farms at Basrah governorate, Southern Iraq using hybrid GIS- based Entropy-TOPSIS and AHP-TOPSIS models," Renewable Energy, Elsevier, vol. 241(C).
    14. Sahoo, Somadutta & Zuidema, Christian & van Stralen, Joost N.P. & Sijm, Jos & Faaij, André, 2022. "Detailed spatial analysis of renewables’ potential and heat: A study of Groningen Province in the northern Netherlands," Applied Energy, Elsevier, vol. 318(C).
    15. Xiao, Sheng & Ren, He & Zhao, Yanling & He, Tingting & Huang, Chunlin & Chen, Qixiang & Zhang, Lifan & An, Yifan & Li, Fashuai & Chen, Yuwei, 2025. "Land reuse for carbon mitigation: Decarbonization potential of solar photovoltaics in China's open-pit mining areas," Land Use Policy, Elsevier, vol. 158(C).
    16. Yılmaz, Kutay & Dinçer, Ali Ersin & Ayhan, Elif N., 2023. "Exploring flood and erosion risk indices for optimal solar PV site selection and assessing the influence of topographic resolution," Renewable Energy, Elsevier, vol. 216(C).
    17. Yilmaz, İsmail & Kocer, Abdulkadir & Aksoy, Ercument, 2024. "Site selection for solar power plants using GIS and fuzzy analytic hierarchy process: Case study of the western mediterranean region of Turkiye," Renewable Energy, Elsevier, vol. 237(PC).
    18. Noorollahi, Younes & Ghenaatpisheh Senani, Ali & Fadaei, Ahmad & Simaee, Mobina & Moltames, Rahim, 2022. "A framework for GIS-based site selection and technical potential evaluation of PV solar farm using Fuzzy-Boolean logic and AHP multi-criteria decision-making approach," Renewable Energy, Elsevier, vol. 186(C), pages 89-104.
    19. Yao, Lingxiang & Guan, Zhiwen & Wang, Yang & Hui, Hongxun & Luo, Shuyu & Jia, Chuyun & You, Xingxing & Xiao, Xianyong, 2025. "Evaluating the feasibility of concentrated solar power as a replacement for coal-fired power in China: A comprehensive comparative analysis," Applied Energy, Elsevier, vol. 377(PA).
    20. Zhang, Zhengjia & Wang, Qingxiang & Liu, Zhengguang & Chen, Qi & Guo, Zhiling & Zhang, Haoran, 2023. "Renew mineral resource-based cities: Assessment of PV potential in coal mining subsidence areas," Applied Energy, Elsevier, vol. 329(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:renene:v:259:y:2026:i:c:s0960148125026898. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.