IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v220y2024ics0960148123015860.html
   My bibliography  Save this article

Global horizontal irradiance prediction model considering the effect of aerosol optical depth based on the Informer model

Author

Listed:
  • Xiu-Yan, Gao
  • Jie-Mei, Liu
  • Yuan, Yuan
  • He-Ping, Tan

Abstract

Accurate and reliable global horizontal irradiance (GHI) prediction helps respond to the volatility of solar power output in advance. Aerosol optical depth (AOD) is the primary factor affecting the GHI in clear skies. In this study, the Informer model was first used to predict the GHI over the next 8 h by considering the AOD, meteorological parameters, and historical GHI data of Beijing as input variables, and the prediction of the GHI without AOD as a feature was calculated. The comparison results showed that the prediction errors of the models that considered AOD were lower than those of the models that did not. Moreover, when the AOD of the predicted day was larger, the difference in the mean absolute percentage error values between the two models was more significant. Hence, we proposed Planck's average AOD as a feature input to the GHI prediction model and found that the accuracy of the model improved regardless of the AOD size on the prediction day. The applicability of the developed model was verified using Golden's data. Overall, these findings indicate that this model can improve computational efficiency while maintaining good predictive performance.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:renene:v:220:y:2024:i:c:s0960148123015860
    DOI: 10.1016/j.renene.2023.119671
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123015860
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2023.119671?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. Zhang, Chu & Hua, Lei & Ji, Chunlei & Shahzad Nazir, Muhammad & Peng, Tian, 2022. "An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine," Applied Energy, Elsevier, vol. 322(C).
    2. Hassan, Muhammed A. & Al-Ghussain, Loiy & Khalil, Adel & Kaseb, Sayed A., 2022. "Self-calibrated hybrid weather forecasters for solar thermal and photovoltaic power plants," Renewable Energy, Elsevier, vol. 188(C), pages 1120-1140.
    3. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    4. 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.
    5. Cheng, Xinghong & Ye, Dong & Shen, Yanbo & Li, Deping & Feng, Jinming, 2022. "Studies on the improvement of modelled solar radiation and the attenuation effect of aerosol using the WRF-Solar model with satellite-based AOD data over north China," Renewable Energy, Elsevier, vol. 196(C), pages 358-365.
    6. 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.
    7. 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. 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).
    2. Puah, Boon Keat & Chong, Lee Wai & Wong, Yee Wan & Begam, K.M. & Khan, Nafizah & Juman, Mohammed Ayoub & Rajkumar, Rajprasad Kumar, 2021. "A regression unsupervised incremental learning algorithm for solar irradiance prediction," Renewable Energy, Elsevier, vol. 164(C), pages 908-925.
    3. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    4. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    5. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
    6. Lilla Barancsuk & Veronika Groma & Dalma Günter & János Osán & Bálint Hartmann, 2024. "Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data," Energies, MDPI, vol. 17(2), pages 1-25, January.
    7. Youssef Karout & Axel Curcio & Julien Eynard & Stéphane Thil & Sylvain Rodat & Stéphane Abanades & Valéry Vuillerme & Stéphane Grieu, 2023. "Model-Based Predictive Control of a Solar Hybrid Thermochemical Reactor for High-Temperature Steam Gasification of Biomass," Clean Technol., MDPI, vol. 5(1), pages 1-23, March.
    8. Hassan, Muhammed A. & Fouad, Aya & Dessoki, Khaled & Al-Ghussain, Loiy & Hamed, Ahmed, 2023. "Performance analyses of supercritical carbon dioxide-based parabolic trough collectors with double-glazed receivers," Renewable Energy, Elsevier, vol. 215(C).
    9. Ye, Wenlian & Wang, Xiaojun & Liu, Yingwen, 2020. "Application of artificial neural network for predicting the dynamic performance of a free piston Stirling engine," Energy, Elsevier, vol. 194(C).
    10. 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).
    11. Ben Ammar, Rim & Ben Ammar, Mohsen & Oualha, Abdelmajid, 2020. "Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems," Renewable Energy, Elsevier, vol. 153(C), pages 1016-1028.
    12. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction," Renewable Energy, Elsevier, vol. 190(C), pages 408-424.
    13. Hasna Hissou & Said Benkirane & Azidine Guezzaz & Mourade Azrour & Abderrahim Beni-Hssane, 2023. "A Novel Machine Learning Approach for Solar Radiation Estimation," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
    14. Acikgoz, Hakan, 2022. "A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting," Applied Energy, Elsevier, vol. 305(C).
    15. Gonzalo Astray & Benedicto Soto & Enrique Barreiro & Juan F. Gálvez & Juan C. Mejuto, 2021. "Machine Learning Applied to the Oxygen-18 Isotopic Composition, Salinity and Temperature/Potential Temperature in the Mediterranean Sea," Mathematics, MDPI, vol. 9(19), pages 1-15, October.
    16. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    17. 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.
    18. Xiong, Jinlin & Peng, Tian & Tao, Zihan & Zhang, Chu & Song, Shihao & Nazir, Muhammad Shahzad, 2023. "A dual-scale deep learning model based on ELM-BiLSTM and improved reptile search algorithm for wind power prediction," Energy, Elsevier, vol. 266(C).
    19. Ochuko Kelvin Overen & Edson Leroy Meyer, 2022. "Solar Energy Resources and Photovoltaic Power Potential of an Underutilised Region: A Case of Alice, South Africa," Energies, MDPI, vol. 15(13), pages 1-29, June.
    20. Logothetis, Stavros-Andreas & Salamalikis, Vasileios & Wilbert, Stefan & Remund, Jan & Zarzalejo, Luis F. & Xie, Yu & Nouri, Bijan & Ntavelis, Evangelos & Nou, Julien & Hendrikx, Niels & Visser, Lenna, 2022. "Benchmarking of solar irradiance nowcast performance derived from all-sky imagers," Renewable Energy, Elsevier, vol. 199(C), pages 246-261.

    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:220:y:2024:i:c:s0960148123015860. 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.