IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v222y2024ics0960148123017834.html
   My bibliography  Save this article

High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning

Author

Listed:
  • Žalik, Mitja
  • Mongus, Domen
  • Lukač, Niko

Abstract

Spatiotemporal assessment of solar potential is one of the most promising solutions to find suitable locations for future photovoltaic systems’ placement. However, accurate assessment of solar potential on large areas can be challenging due to missing data or computational complexity. In this paper, a fully convolutional neural network based method for high-resolution spatiotemporal assessment of solar potential by using remote sensing data is presented. The method is trained and validated on the area of 32 km2 of the Maribor city, Slovenia, and tested on the 6 different locations in Slovenia and Germany. The proposed method was tested against the simulation algorithm, which utilized isotropic Liu Jordan diffuse model or anisotropic Perez model with sky view factor based shading. On average, a normalized root mean square error (NRMSE) of 6.06% and mean absolute percentage error (MAPE) of 4.29% was achieved in test locations against the simulation based on Liu-Jordan model. When training the fully convolutional neural network models against the ground truth, generated with more advanced Perez diffuse model, the average NRMSE was 8.37% and MAPE of 10.90% was achieved across all test locations. Additionally, it was shown that the proposed method can assess solar potential in high-resolution from the input in lower resolution with higher accuracy than the simulation algorithm, while being up to 1500-times faster.

Suggested Citation

  • Žalik, Mitja & Mongus, Domen & Lukač, Niko, 2024. "High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning," Renewable Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:renene:v:222:y:2024:i:c:s0960148123017834
    DOI: 10.1016/j.renene.2023.119868
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123017834
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119868?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Heo, Jae & Jung, Jaehoon & Kim, Byungil & Han, SangUk, 2020. "Digital elevation model-based convolutional neural network modeling for searching of high solar energy regions," Applied Energy, Elsevier, vol. 262(C).
    2. Libin Jiao & Rongfang Bie & Hao Wu & Yu Wei & Jixin Ma & Anton Umek & Anton Kos, 2018. "Golf swing classification with multiple deep convolutional neural networks," International Journal of Distributed Sensor Networks, , vol. 14(10), pages 15501477188, October.
    3. Lukač, Niko & Špelič, Denis & Štumberger, Gorazd & Žalik, Borut, 2020. "Optimisation for large-scale photovoltaic arrays’ placement based on Light Detection And Ranging data," Applied Energy, Elsevier, vol. 263(C).
    4. Ouammi, Ahmed & Zejli, Driss & Dagdougui, Hanane & Benchrifa, Rachid, 2012. "Artificial neural network analysis of Moroccan solar potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4876-4889.
    5. Zhang, Chen & Li, Zhixin & Jiang, Haihua & Luo, Yongqiang & Xu, Shen, 2021. "Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China," Applied Energy, Elsevier, vol. 283(C).
    6. Zhong, Teng & Zhang, Zhixin & Chen, Min & Zhang, Kai & Zhou, Zixuan & Zhu, Rui & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2021. "A city-scale estimation of rooftop solar photovoltaic potential based on deep learning," Applied Energy, Elsevier, vol. 298(C).
    7. Kutlu, Elif Ceren & Durusoy, Beyza & Ozden, Talat & Akinoglu, Bulent G., 2022. "Technical potential of rooftop solar photovoltaic for Ankara," Renewable Energy, Elsevier, vol. 185(C), pages 779-789.
    8. Jung, Jaehoon & Han, SangUk & Kim, Byungil, 2019. "Digital numerical map-oriented estimation of solar energy potential for site selection of photovoltaic solar panels on national highway slopes," Applied Energy, Elsevier, vol. 242(C), pages 57-68.
    9. Sebastian Krapf & Nils Kemmerzell & Syed Khawaja Haseeb Uddin & Manuel Hack Vázquez & Fabian Netzler & Markus Lienkamp, 2021. "Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning," Energies, MDPI, vol. 14(13), pages 1-22, June.
    10. Tian, B. & Loonen, R.C.G.M. & Bognár, Á. & Hensen, J.L.M., 2022. "Impacts of surface model generation approaches on raytracing-based solar potential estimation in urban areas," Renewable Energy, Elsevier, vol. 198(C), pages 804-824.
    11. Walch, Alina & Castello, Roberto & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2020. "Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty," Applied Energy, Elsevier, vol. 262(C).
    12. Huang, Zhaojian & Mendis, Thushini & Xu, Shen, 2019. "Urban solar utilization potential mapping via deep learning technology: A case study of Wuhan, China," Applied Energy, Elsevier, vol. 250(C), pages 283-291.
    13. Hofierka, Jaroslav & Kaňuk, Ján, 2009. "Assessment of photovoltaic potential in urban areas using open-source solar radiation tools," Renewable Energy, Elsevier, vol. 34(10), pages 2206-2214.
    14. Kim, Hanjin & Ku, Jiyoon & Kim, Sung-Min & Park, Hyeong-Dong, 2022. "A new GIS-based algorithm to estimate photovoltaic potential of solar train: Case study in Gyeongbu line, Korea," Renewable Energy, Elsevier, vol. 190(C), pages 713-729.
    15. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
    16. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(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. 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).
    2. Gassar, Abdo Abdullah Ahmed & Cha, Seung Hyun, 2021. "Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales," Applied Energy, Elsevier, vol. 291(C).
    3. 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).
    4. 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).
    5. Sebastian Krapf & Nils Kemmerzell & Syed Khawaja Haseeb Uddin & Manuel Hack Vázquez & Fabian Netzler & Markus Lienkamp, 2021. "Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning," Energies, MDPI, vol. 14(13), pages 1-22, June.
    6. Aslani, Mohammad & Seipel, Stefan, 2022. "Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment," Applied Energy, Elsevier, vol. 306(PA).
    7. Jiang, Hou & Zhang, Xiaotong & Yao, Ling & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2023. "High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles," Applied Energy, Elsevier, vol. 348(C).
    8. 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).
    9. Sun, Tao & Shan, Ming & Rong, Xing & Yang, Xudong, 2022. "Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images," Applied Energy, Elsevier, vol. 315(C).
    10. Chen, Qi & Li, Xinyuan & Zhang, Zhengjia & Zhou, Chao & Guo, Zhiling & Liu, Zhengguang & Zhang, Haoran, 2023. "Remote sensing of photovoltaic scenarios: Techniques, applications and future directions," Applied Energy, Elsevier, vol. 333(C).
    11. Ding, Feng & Yang, Jianping & Zhou, Zan, 2023. "Economic profits and carbon reduction potential of photovoltaic power generation for China's high-speed railway infrastructure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    12. Wenbo Cui & Xiangang Peng & Jinhao Yang & Haoliang Yuan & Loi Lei Lai, 2023. "Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image," Energies, MDPI, vol. 16(18), pages 1-17, September.
    13. Elham Fakhraian & Marc Alier & Francesc Valls Dalmau & Alireza Nameni & Maria José Casañ Guerrero, 2021. "The Urban Rooftop Photovoltaic Potential Determination," Sustainability, MDPI, vol. 13(13), pages 1-18, July.
    14. Lukač, Niko & Mongus, Domen & Žalik, Borut & Štumberger, Gorazd & Bizjak, Marko, 2024. "Novel GPU-accelerated high-resolution solar potential estimation in urban areas by using a modified diffuse irradiance model," Applied Energy, Elsevier, vol. 353(PA).
    15. 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).
    16. Heo, Jae & Song, Kwonsik & Han, SangUk & Lee, Dong-Eun, 2021. "Multi-channel convolutional neural network for integration of meteorological and geographical features in solar power forecasting," Applied Energy, Elsevier, vol. 295(C).
    17. Job Taminiau & John Byrne & Jongkyu Kim & Min‐Hwi Kim & Jeongseok Seo, 2022. "Inferential‐ and measurement‐based methods to estimate rooftop “solar city” potential in megacity Seoul, South Korea," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 11(5), September.
    18. Enrique Fuster-Palop & Carlos Prades-Gil & Ximo Masip & J. D. Viana-Fons & Jorge Payá, 2023. "Techno-Economic Potential of Urban Photovoltaics: Comparison of Net Billing and Net Metering in a Mediterranean Municipality," Energies, MDPI, vol. 16(8), pages 1-32, April.
    19. Chen, Zhe & Yang, Bisheng & Zhu, Rui & Dong, Zhen, 2024. "City-scale solar PV potential estimation on 3D buildings using multi-source RS data: A case study in Wuhan, China," Applied Energy, Elsevier, vol. 359(C).
    20. Babak Ranjgar & Alessandro Niccolai, 2023. "Large-Scale Rooftop Solar Photovoltaic Power Production Potential Assessment: A Case Study for Tehran Metropolitan Area, Iran," Energies, MDPI, vol. 16(20), pages 1-14, October.

    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:222:y:2024:i:c:s0960148123017834. 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.