IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v352y2023ics0306261923013430.html
   My bibliography  Save this article

Global and direct solar irradiance estimation using deep learning and selected spectral satellite images

Author

Listed:
  • Chen, Shanlin
  • Li, Chengxi
  • Xie, Yuying
  • Li, Mengying

Abstract

To fully exploit the spectral information of modern geostationary satellites, this work proposes a deep learning framework using convolutional neural networks (CNNs) and attention mechanism for 5-min ground-level global horizontal irradiance (GHI) and direct normal irradiance (DNI) estimations. The inputs are spectral satellite images with the target ground station in the center, and the labels are irradiance measurements normalized by their clear-sky estimations. The use of CNNs and attention mechanism aims to better extract the spatial information for estimating ground-level solar irradiance. To improve the modeling efficiency, only a subset of spectral bands is selected based on correlation analysis, which has comparable performance with the usage of all satellite bands. The results show that the proposed method produces GHI estimation with a normalized root mean squared error (nRMSE) of 20.57% and a normalized mean bias error (nMBE) of −2.04%, and the DNI estimation has an nRMSE of 23.63% and the nMBE is 0.36%. Compared with the national solar radiation database (NSRDB), GHI and DNI estimations of the proposed method has the nRMSE reduction of 5.15% and 13.77%, respectively. Meanwhile, the proposed models generally yield better GHI and DNI estimations under different intervals of clear-sky index than NSRDB. The combination of deep learning and remote sensing shows potential in better extracting the cloud information via multispectral satellite images, which can better support solar resource assessment, especially for cloudy conditions.

Suggested Citation

  • Chen, Shanlin & Li, Chengxi & Xie, Yuying & Li, Mengying, 2023. "Global and direct solar irradiance estimation using deep learning and selected spectral satellite images," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013430
    DOI: 10.1016/j.apenergy.2023.121979
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923013430
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121979?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. Chu, Yinghao & Li, Mengying & Coimbra, Carlos F.M., 2016. "Sun-tracking imaging system for intra-hour DNI forecasts," Renewable Energy, Elsevier, vol. 96(PA), pages 792-799.
    2. Jiang, Hou & Lu, Ning & Qin, Jun & Tang, Wenjun & Yao, Ling, 2019. "A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    3. Chen, Jiang & Zhu, Weining & Yu, Qian, 2021. "Estimating half-hourly solar radiation over the Continental United States using GOES-16 data with iterative random forest," Renewable Energy, Elsevier, vol. 178(C), pages 916-929.
    4. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Zambrano, Andres Felipe & Giraldo, Luis Felipe, 2020. "Solar irradiance forecasting models without on-site training measurements," Renewable Energy, Elsevier, vol. 152(C), pages 557-566.
    6. Nespoli, Alfredo & Niccolai, Alessandro & Ogliari, Emanuele & Perego, Giovanni & Collino, Elena & Ronzio, Dario, 2022. "Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery," Applied Energy, Elsevier, vol. 305(C).
    7. Yagli, Gokhan Mert & Yang, Dazhi & Gandhi, Oktoviano & Srinivasan, Dipti, 2020. "Can we justify producing univariate machine-learning forecasts with satellite-derived solar irradiance?," Applied Energy, Elsevier, vol. 259(C).
    8. Lu, Ning & Qin, Jun & Yang, Kun & Sun, Jiulin, 2011. "A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data," Energy, Elsevier, vol. 36(5), pages 3179-3188.
    9. Li, Mengying & Chu, Yinghao & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2016. "Quantitative evaluation of the impact of cloud transmittance and cloud velocity on the accuracy of short-term DNI forecasts," Renewable Energy, Elsevier, vol. 86(C), pages 1362-1371.
    10. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.
    11. Tapakis, R. & Charalambides, A.G., 2014. "Enhanced values of global irradiance due to the presence of clouds in Eastern Mediterranean," Renewable Energy, Elsevier, vol. 62(C), pages 459-467.
    12. Liu, Fa & Wang, Xunming & Sun, Fubao & Wang, Hong, 2022. "Correct and remap solar radiation and photovoltaic power in China based on machine learning models," Applied Energy, Elsevier, vol. 312(C).
    13. Ajith, Meenu & Martínez-Ramón, Manel, 2021. "Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data," Applied Energy, Elsevier, vol. 294(C).
    14. Chen, Shanlin & Li, Mengying, 2022. "Improved turbidity estimation from local meteorological data for solar resourcing and forecasting applications," Renewable Energy, Elsevier, vol. 189(C), pages 259-272.
    15. Chen, Shanlin & Liang, Zhaojian & Dong, Peixin & Guo, Su & Li, Mengying, 2023. "A transferable turbidity estimation method for estimating clear-sky solar irradiance," Renewable Energy, Elsevier, vol. 206(C), pages 635-644.
    16. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    17. Bright, Jamie M. & Sun, Xixi & Gueymard, Christian A. & Acord, Brendan & Wang, Peng & Engerer, Nicholas A., 2020. "Bright-Sun: A globally applicable 1-min irradiance clear-sky detection model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 121(C).
    18. Salazar, Germán & Gueymard, Christian & Galdino, Janis Bezerra & de Castro Vilela, Olga & Fraidenraich, Naum, 2020. "Solar irradiance time series derived from high-quality measurements, satellite-based models, and reanalyses at a near-equatorial site in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fan, Jie & Wang, Lei & Zhang, Zhen & Liu, Ming & Cao, Xinyue & Gong, Min & Tang, Qiuping & She, Chao & Qi, Fang & Si, Hucheng & Song, Dan & Zhang, Qiyuan & Xie, Peng, 2024. "Approaches to improve the accuracy of estimating the diffuse fraction of 1-min resolution global horizontal irradiance using cloud images," Renewable Energy, Elsevier, vol. 230(C).
    2. Hanif, M.F. & Mi, J., 2024. "Harnessing AI for solar energy: Emergence of transformer models," Applied Energy, Elsevier, vol. 369(C).

    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. Chen, Shanlin & Liang, Zhaojian & Dong, Peixin & Guo, Su & Li, Mengying, 2023. "A transferable turbidity estimation method for estimating clear-sky solar irradiance," Renewable Energy, Elsevier, vol. 206(C), pages 635-644.
    2. Yang, Dazhi, 2022. "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    3. Chu, Yinghao & Wang, Yiling & Yang, Dazhi & Chen, Shanlin & Li, Mengying, 2024. "A review of distributed solar forecasting with remote sensing and deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    4. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
    5. Nie, Yuhao & Li, Xiatong & Paletta, Quentin & Aragon, Max & Scott, Andea & Brandt, Adam, 2024. "Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    6. Liu, Jingxuan & Zang, Haixiang & Ding, Tao & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2023. "Harvesting spatiotemporal correlation from sky image sequence to improve ultra-short-term solar irradiance forecasting," Renewable Energy, Elsevier, vol. 209(C), pages 619-631.
    7. Liao, Zhouyi & Coimbra, Carlos F.M., 2024. "Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks," Renewable Energy, Elsevier, vol. 232(C).
    8. Ren, Simiao & Hu, Wayne & Bradbury, Kyle & Harrison-Atlas, Dylan & Malaguzzi Valeri, Laura & Murray, Brian & Malof, Jordan M., 2022. "Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis," Applied Energy, Elsevier, vol. 326(C).
    9. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    10. Chen, Shanlin & Li, Mengying, 2022. "Improved turbidity estimation from local meteorological data for solar resourcing and forecasting applications," Renewable Energy, Elsevier, vol. 189(C), pages 259-272.
    11. Liu, Bai & Yang, Dazhi & Mayer, Martin János & Coimbra, Carlos F.M. & Kleissl, Jan & Kay, Merlinde & Wang, Wenting & Bright, Jamie M. & Xia, Xiang’ao & Lv, Xin & Srinivasan, Dipti & Wu, Yan & Beyer, H, 2023. "Predictability and forecast skill of solar irradiance over the contiguous United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    12. Guo, Su & Liu, Deyou & Chen, Xingying & Chu, Yinghao & Xu, Chang & Liu, Qunming & Zhou, Ling, 2017. "Model and control scheme for recirculation mode direct steam generation parabolic trough solar power plants," Applied Energy, Elsevier, vol. 202(C), pages 700-714.
    13. Fan, Jie & Wang, Lei & Zhang, Zhen & Liu, Ming & Cao, Xinyue & Gong, Min & Tang, Qiuping & She, Chao & Qi, Fang & Si, Hucheng & Song, Dan & Zhang, Qiyuan & Xie, Peng, 2024. "Approaches to improve the accuracy of estimating the diffuse fraction of 1-min resolution global horizontal irradiance using cloud images," Renewable Energy, Elsevier, vol. 230(C).
    14. Ruiz-Arias, José A., 2023. "SPARTA: Solar parameterization for the radiative transfer of the cloudless atmosphere," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    15. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    16. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2022. "A network of sky imagers for spatial solar irradiance assessment," Renewable Energy, Elsevier, vol. 187(C), pages 1009-1019.
    17. Niu, Yinsen & Song, Jifeng & Zou, Lianglin & Yan, Zixuan & Lin, Xilong, 2024. "Cloud detection method using ground-based sky images based on clear sky library and superpixel local threshold," Renewable Energy, Elsevier, vol. 226(C).
    18. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(C).
    19. Lin, Fan & Zhang, Yao & Wang, Jianxue, 2023. "Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods," International Journal of Forecasting, Elsevier, vol. 39(1), pages 244-265.
    20. Garcia, Dário & Liang, Dawei & Almeida, Joana & Catela, Miguel & Costa, Hugo & Tibúrcio, Bruno D. & Guillot, Emmanuel & Vistas, Cláudia R., 2023. "Lowest-threshold solar laser operation under cloudy sky condition," Renewable Energy, Elsevier, vol. 210(C), pages 127-133.

    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:appene:v:352:y:2023:i:c:s0306261923013430. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.