IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i17p7462-d1466471.html
   My bibliography  Save this article

Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision

Author

Listed:
  • Mohamed Khalifa Boutahir

    (STI Laboratory, T-IDMS Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Errachidia 52000, Morocco)

  • Yousef Farhaoui

    (STI Laboratory, T-IDMS Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Errachidia 52000, Morocco)

  • Mourade Azrour

    (STI Laboratory, T-IDMS Faculty of Sciences and Techniques of Errachidia, Moulay Ismail University of Meknès, Errachidia 52000, Morocco)

  • Ahmed Sedik

    (Smart Systems Engineering Laboratory, Communications and Networks Engineering Department, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia
    Department of the Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33511, Egypt)

  • Moustafa M. Nasralla

    (Smart Systems Engineering Laboratory, Communications and Networks Engineering Department, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia)

Abstract

Accurate solar power generation forecasting is paramount for optimizing renewable energy systems and ensuring sustainability in our evolving energy landscape. This study introduces a pioneering approach that synergistically integrates Boosting Cascade Forest and multi-class-grained scanning techniques to enhance the precision of solar farm power output predictions significantly. While Boosting Cascade Forest excels in capturing intricate, nonlinear variable interactions through ensemble decision tree learning, multi-class-grained scanning reveals fine-grained patterns within time-series data. Evaluation with real-world solar farm data demonstrates exceptional performance, reflected in low error metrics (mean absolute error, 0.0016; root mean square error 0.0036) and an impressive R-squared score of 99.6% on testing data. This research represents the inaugural application of these advanced techniques to solar generation forecasting, highlighting their potential to revolutionize renewable energy integration, streamline maintenance, and reduce costs. Opportunities for further refinement of ensemble models and exploration of probabilistic forecasting methods are also discussed, underscoring the significance of this work in advancing solar forecasting techniques for a sustainable energy future.

Suggested Citation

  • Mohamed Khalifa Boutahir & Yousef Farhaoui & Mourade Azrour & Ahmed Sedik & Moustafa M. Nasralla, 2024. "Advancing Solar Power Forecasting: Integrating Boosting Cascade Forest and Multi-Class-Grained Scanning for Enhanced Precision," Sustainability, MDPI, vol. 16(17), pages 1-20, August.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7462-:d:1466471
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/17/7462/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/17/7462/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.
    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. Jerome G. Gacu & Junrey D. Garcia & Eddie G. Fetalvero & Merian P. Catajay-Mani & Cris Edward F. Monjardin, 2023. "Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration," Energies, MDPI, vol. 16(18), pages 1-28, September.
    2. Aleksandr Gevorgian & Giovanni Pernigotto & Andrea Gasparella, 2024. "Addressing Data Scarcity in Solar Energy Prediction with Machine Learning and Augmentation Techniques," Energies, MDPI, vol. 17(14), pages 1-19, July.
    3. Jinming Gao & Xianlong Su & Changsu Kim & Kerang Cao & Hoekyung Jung, 2024. "A Parallel Prediction Model for Photovoltaic Power Using Multi-Level Attention and Similar Day Clustering," Energies, MDPI, vol. 17(16), pages 1-17, August.
    4. Yang Liu & Tianxing Yang & Liwei Tian & Bincheng Huang & Jiaming Yang & Zihan Zeng, 2024. "Ada-XG-CatBoost: A Combined Forecasting Model for Gross Ecosystem Product (GEP) Prediction," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    5. Oubah Isman Okieh & Serhat Seker & Seckin Gokce & Martin Dennenmoser, 2024. "An Enhanced Forecasting Method of Daily Solar Irradiance in Southwestern France: A Hybrid Nonlinear Autoregressive with Exogenous Inputs with Long Short-Term Memory Approach," Energies, MDPI, vol. 17(16), pages 1-21, August.
    6. Zhuoyuan Lyu & Ying Shen & Yu Zhao & Tao Hu, 2023. "Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model," Sustainability, MDPI, vol. 15(20), pages 1-18, 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:gam:jsusta:v:16:y:2024:i:17:p:7462-:d:1466471. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.