IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v227y2024ics0960148124006335.html
   My bibliography  Save this article

Global horizontal irradiance prediction model for multi-site fusion under different aerosol types

Author

Listed:
  • Gao, Xiu-Yan
  • Huang, Chun-Lin
  • Zhang, Zhen-Huan
  • Chen, Qi-Xiang
  • Zheng, Yu
  • Fu, Di-Song
  • Yuan, Yuan

Abstract

Accurate prediction of global horizontal irradiance (GHI) is crucial for anticipating the volatility of solar power output. Under clear-sky conditions, aerosols play a significant role in influencing GHI, with different types of aerosols exhibiting distinct radiation effects. In this study, we employed the Planck mean aerosol optical depth (AOD) —Informer model to forecast future GHI, utilizing historical data of AOD, meteorological parameters, and GHI as input variables. Two data-fusion schemes were proposed, and the calculation results indicate that Scheme 1, organized in the order of Xianghe—Beijing—Shangdianzi (XBS) based on the day as a unit, demonstrates superior prediction performance. Additionally, the site information was blurred, allowing for the flexible selection of input and output lengths. The calculation results remained stable and reliable, suggesting that the inclusion of fuzzy site information does not degrade the prediction effect. Building upon this, we categorized aerosols into three types based on their time span and data volume. GHI predictions for different aerosol types revealed that the mean square error (0.145) for marine aerosols were significantly lower than those for the other types. The GHI predictions for continental aerosols were closest to the true values.

Suggested Citation

  • Gao, Xiu-Yan & Huang, Chun-Lin & Zhang, Zhen-Huan & Chen, Qi-Xiang & Zheng, Yu & Fu, Di-Song & Yuan, Yuan, 2024. "Global horizontal irradiance prediction model for multi-site fusion under different aerosol types," Renewable Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:renene:v:227:y:2024:i:c:s0960148124006335
    DOI: 10.1016/j.renene.2024.120565
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124006335
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.120565?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. Rusen, Selmin Ener & Hammer, Annette & Akinoglu, Bulent G., 2013. "Coupling satellite images with surface measurements of bright sunshine hours to estimate daily solar irradiation on horizontal surface," Renewable Energy, Elsevier, vol. 55(C), pages 212-219.
    2. Dasari, Hari Prasad & Desamsetti, Srinivas & Langodan, Sabique & Attada, Raju & Kunchala, Ravi Kumar & Viswanadhapalli, Yesubabu & Knio, Omar & Hoteit, Ibrahim, 2019. "High-resolution assessment of solar energy resources over the Arabian Peninsula," Applied Energy, Elsevier, vol. 248(C), pages 354-371.
    3. 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.
    4. Ener Rusen, Selmin & Konuralp, Aycan, 2020. "Quality control of diffuse solar radiation component with satellite-based estimation methods," Renewable Energy, Elsevier, vol. 145(C), pages 1772-1779.
    5. Caldas, M. & Alonso-Suárez, R., 2019. "Very short-term solar irradiance forecast using all-sky imaging and real-time irradiance measurements," Renewable Energy, Elsevier, vol. 143(C), pages 1643-1658.
    6. Ener Rusen, Selmin & Hammer, Annette & Akinoglu, Bulent G., 2013. "Estimation of daily global solar irradiation by coupling ground measurements of bright sunshine hours to satellite imagery," Energy, Elsevier, vol. 58(C), pages 417-425.
    7. Kumari, Pratima & Toshniwal, Durga, 2021. "Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Elsevier, vol. 295(C).
    8. Nur Wardhyana Yahya & D.M. Reddy Prasad & Dakshinamoorthy Sathiyamoorthy & Rajashekhar Pendyala, 2021. "Prospects and roadmaps for harvesting solar thermal power in tropical Brunei Darussalam," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 43(5/6), pages 616-642.
    9. Qun Niu & Han Wang & Ziyuan Sun & Zhile Yang, 2019. "An Improved Bare Bone Multi-Objective Particle Swarm Optimization Algorithm for Solar Thermal Power Plants," Energies, MDPI, vol. 12(23), pages 1-22, November.
    10. Sibtain, Muhammad & Li, Xianshan & Saleem, Snoober & Ain, Qurat-ul- & Shi, Qiang & Li, Fei & Saeed, Muhammad & Majeed, Fatima & Shah, Syed Shoaib Ahmed & Saeed, Muhammad Hammad, 2022. "Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models," Renewable Energy, Elsevier, vol. 196(C), pages 648-682.
    11. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    12. 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).
    13. Jia, Dongyu & Yang, Liwei & Lv, Tao & Liu, Weiping & Gao, Xiaoqing & Zhou, Jiaxin, 2022. "Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions," Renewable Energy, Elsevier, vol. 187(C), pages 896-906.
    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. Starke, Allan R. & Lemos, Leonardo F.L. & Barni, Cristian M. & Machado, Rubinei D. & Cardemil, José M. & Boland, John & Colle, Sergio, 2021. "Assessing one-minute diffuse fraction models based on worldwide climate features," Renewable Energy, Elsevier, vol. 177(C), pages 700-714.
    2. Li, Peidu & Gao, Xiaoqing & Li, Zhenchao & Zhou, Xiyin, 2022. "Effect of the temperature difference between land and lake on photovoltaic power generation," Renewable Energy, Elsevier, vol. 185(C), pages 86-95.
    3. Xiu-Yan, Gao & Jie-Mei, Liu & Yuan, Yuan & He-Ping, Tan, 2024. "Global horizontal irradiance prediction model considering the effect of aerosol optical depth based on the Informer model," Renewable Energy, Elsevier, vol. 220(C).
    4. Boukelia, T.E. & Ghellab, A. & Laouafi, A. & Bouraoui, A. & Kabar, Y., 2020. "Cooling performances time series of CSP plants: Calculation and analysis using regression and ANN models," Renewable Energy, Elsevier, vol. 157(C), pages 809-827.
    5. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    6. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(C).
    7. Yıldırım, H. Başak & Teke, Ahmet & Antonanzas-Torres, Fernando, 2018. "Evaluation of classical parametric models for estimating solar radiation in the Eastern Mediterranean region of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2053-2065.
    8. Han, Tian & Li, Ruimeng & Wang, Xiao & Wang, Ying & Chen, Kang & Peng, Huaiwu & Gao, Zhenxin & Wang, Nannan & Peng, Qinke, 2024. "Intra-hour solar irradiance forecasting using topology data analysis and physics-driven deep learning," Renewable Energy, Elsevier, vol. 224(C).
    9. Jiang, Chengcheng & Zhu, Qunzhi, 2023. "Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer," Applied Energy, Elsevier, vol. 348(C).
    10. 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).
    11. Ogliari, Emanuele & Sakwa, Maciej & Cusa, Paolo, 2024. "Enhanced Convolutional Neural Network for solar radiation nowcasting: All-Sky camera infrared images embedded with exogeneous parameters," Renewable Energy, Elsevier, vol. 221(C).
    12. Hanif, M.F. & Mi, J., 2024. "Harnessing AI for solar energy: Emergence of transformer models," Applied Energy, Elsevier, vol. 369(C).
    13. Clauzel, Léo & Anquetin, Sandrine & Lavaysse, Christophe & Tremoy, Guillaume & Raynaud, Damien, 2024. "West African operational daily solar forecast errors and their link with meteorological conditions," Renewable Energy, Elsevier, vol. 224(C).
    14. Hoyos-Gómez, Laura S. & Ruiz-Muñoz, Jose F. & Ruiz-Mendoza, Belizza J., 2022. "Short-term forecasting of global solar irradiance in tropical environments with incomplete data," Applied Energy, Elsevier, vol. 307(C).
    15. Neupane, Deependra & Kafle, Sagar & Karki, Kaji Ram & Kim, Dae Hyun & Pradhan, Prajal, 2022. "Solar and wind energy potential assessment at provincial level in Nepal: Geospatial and economic analysis," Renewable Energy, Elsevier, vol. 181(C), pages 278-291.
    16. Gueymard, Christian A. & Bright, Jamie M. & Lingfors, David & Habte, Aron & Sengupta, Manajit, 2019. "A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 412-427.
    17. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    18. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    19. Bracken, Cameron & Voisin, Nathalie & Burleyson, Casey D. & Campbell, Allison M. & Hou, Z. Jason & Broman, Daniel, 2024. "Standardized benchmark of historical compound wind and solar energy droughts across the Continental United States," Renewable Energy, Elsevier, vol. 220(C).
    20. Zimmerman, Ryan & Panda, Anurag & Bulović, Vladimir, 2020. "Techno-economic assessment and deployment strategies for vertically-mounted photovoltaic panels," Applied Energy, Elsevier, vol. 276(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:eee:renene:v:227:y:2024:i:c:s0960148124006335. 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.