IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i17p7340-d1464409.html
   My bibliography  Save this article

A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting

Author

Listed:
  • Yongning Zhang

    (School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Xiaoying Ren

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Fei Zhang

    (School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China
    School of New Energy, North China Electric Power University, Beijing 102206, China)

  • Yulei Liu

    (Inner Mongolia Huadian Huitengxile Wind Power Co., Ulanqab 013550, China)

  • Jierui Li

    (School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

Abstract

Ultra-short-term photovoltaic (PV) power forecasting is crucial in the scheduling and functioning of contemporary electrical systems, playing a key role in promoting renewable energy integration and sustainability. In this paper, a novel hybrid model, termed AI_VMD-HS_CNN-BiLSTM-A, is introduced to tackle the challenges associated with the volatility and unpredictability inherent in PV power output. Firstly, Akaike information criterion variational mode decomposition (AI_VMD) integrates the Akaike information criterion with variational mode decomposition (VMD) and reduces data complexity, enhancing grid optimization and energy efficiency. The adaptive selection of optimal parameters enhances VMD decomposition performance, supporting sustainable energy management. Secondly, the hierarchical scale-transform convolutional architecture (HS_CNN) supplements the traditional convolutional neural network (CNN) with two channels featuring distinct dilation rates, thereby extracting dual levels of time-scale information for a more comprehensive data representation. Finally, a bidirectional long short-term memory neural network (BiLSTM) with an attentional mechanism combines past and future data to enable more accurate forecasts, aiding in carbon reduction and smart grid advancements. Experimentation with data from the Alice Springs PV plant in Australia demonstrates that the proposed AI_VMD-HS_CNN-BiLSTM-A model exhibits superior adaptability and accuracy in multiple time-scale forecasting compared to the baseline models. This approach is important for decision-making and scheduling in grid-connected photovoltaic systems, enhancing energy resilience and promoting the sustainable development of renewable energy.

Suggested Citation

  • Yongning Zhang & Xiaoying Ren & Fei Zhang & Yulei Liu & Jierui Li, 2024. "A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 16(17), pages 1-22, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7340-:d:1464409
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/17/7340/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/17/7340/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
    2. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    3. Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
    4. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
    5. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(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. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
    2. Hu, Yue & Liu, Hanjing & Wu, Senzhen & Zhao, Yuan & Wang, Zhijin & Liu, Xiufeng, 2024. "Temporal collaborative attention for wind power forecasting," Applied Energy, Elsevier, vol. 357(C).
    3. Wang, Yun & Song, Mengmeng & Yang, Dazhi, 2024. "Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph," Energy, Elsevier, vol. 289(C).
    4. Niu, Yunbo & Wang, Jianzhou & Zhang, Ziyuan & Luo, Tianrui & Liu, Jingjiang, 2024. "De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure," Applied Energy, Elsevier, vol. 353(PB).
    5. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    6. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Prediction of fluctuation loads based on GARCH family-CatBoost-CNNLSTM," Energy, Elsevier, vol. 263(PE).
    7. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    8. Yifei Chen & Zhihan Fu, 2023. "Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    9. Siting Li & Huafeng Cai, 2024. "Short-Term Power Load Forecasting Using a VMD-Crossformer Model," Energies, MDPI, vol. 17(11), pages 1-18, June.
    10. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
    11. Zheng Wan & Hui Li, 2023. "Short-Term Power Load Forecasting Based on Feature Filtering and Error Compensation under Imbalanced Samples," Energies, MDPI, vol. 16(10), pages 1-22, May.
    12. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
    13. Jiang, Ben & Li, Yu & Rezgui, Yacine & Zhang, Chengyu & Wang, Peng & Zhao, Tianyi, 2024. "Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings," Energy, Elsevier, vol. 299(C).
    14. Fei Zhang & Xiaoying Ren & Guidong Yang & Shulong Zhang & Yongqian Liu, 2024. "Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power," Energies, MDPI, vol. 17(6), pages 1-20, March.
    15. Hong Wu & Haipeng Liu & Huaiping Jin & Yanping He, 2024. "Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data," Energies, MDPI, vol. 17(18), pages 1-19, September.
    16. Yang, Kailing & Zhang, Xi & Luo, Haojia & Hou, Xianping & Lin, Yu & Wu, Jingyu & Yu, Liang, 2024. "Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting," Energy, Elsevier, vol. 298(C).
    17. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    18. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    19. Wen, Yan & Pan, Su & Li, Xinxin & Li, Zibo & Wen, Wuzhenghong, 2024. "Improving multi-site photovoltaic forecasting with relevance amplification: DeepFEDformer-based approach," Energy, Elsevier, vol. 299(C).
    20. Wu, Han & Gao, Xiao-Zhi & Heng, Jia-Ni, 2024. "Bio-multisensory-inspired gate-attention coordination model for forecasting short-term significant wave height," Energy, Elsevier, vol. 294(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:gam:jsusta:v:16:y:2024:i:17:p:7340-:d:1464409. 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.