IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i17p4434-d1471076.html

Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm

Author

Listed:
  • Yuhan Wu

    (College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China)

  • Chun Xiang

    (School of Mechanical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Heng Qian

    (School of Mechanical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310018, China)

  • Peijian Zhou

    (College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China)

Abstract

To enhance the stability of photovoltaic power grid integration and improve power prediction accuracy, a photovoltaic power prediction method based on an improved snow ablation optimization algorithm (Good Point and Vibration Snow Ablation Optimizer, GVSAO) and Bi-directional Long Short-Term Memory (Bi-LSTM) network is proposed. Weather data is divided into three typical categories using K-means clustering, and data normalization is performed using the minmax method. The key structural parameters of Bi-LSTM, such as the feature dimension at each time step and the number of hidden units in each LSTM layer, are optimized based on the Good Point and Vibration strategy. A prediction model is constructed based on GVSAO-Bi-LSTM, and typical test functions are selected to analyze and evaluate the improved model. The research results show that the average absolute percentage error of the GVSAO-Bi-LSTM prediction model under sunny, cloudy, and rainy weather conditions are 4.75%, 5.41%, and 14.37%, respectively. Compared with other methods, the prediction results of this model are more accurate, verifying its effectiveness.

Suggested Citation

  • Yuhan Wu & Chun Xiang & Heng Qian & Peijian Zhou, 2024. "Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm," Energies, MDPI, vol. 17(17), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4434-:d:1471076
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/17/4434/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/17/4434/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hosenuzzaman, M. & Rahim, N.A. & Selvaraj, J. & Hasanuzzaman, M. & Malek, A.B.M.A. & Nahar, A., 2015. "Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 284-297.
    2. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    3. Bowen Zhou & Xinyu Chen & Guangdi Li & Peng Gu & Jing Huang & Bo Yang, 2023. "XGBoost–SFS and Double Nested Stacking Ensemble Model for Photovoltaic Power Forecasting under Variable Weather Conditions," Sustainability, MDPI, vol. 15(17), pages 1-24, September.
    4. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    5. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    6. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(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. Li, Jiaqian & Rao, Congjun & Gao, Mingyun & Xiao, Xinping & Goh, Mark, 2025. "Efficient calculation of distributed photovoltaic power generation power prediction via deep learning," Renewable Energy, Elsevier, vol. 246(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. Zhang, Zongbin & Huang, Xiaoqiao & Li, Chengli & Cheng, Feiyan & Tai, Yonghang, 2025. "CRAformer: A cross-residual attention transformer for solar irradiation multistep forecasting," Energy, Elsevier, vol. 320(C).
    2. Gong, Bin & An, Aimin & Shi, Yaoke & Guan, Haijiao & Jia, Wenchao & Yang, Fazhi, 2024. "An interpretable hybrid spatiotemporal fusion method for ultra-short-term photovoltaic power prediction," Energy, Elsevier, vol. 308(C).
    3. Wan, Hang & Wang, Jiasong & Gan, Quan & Xia, Yaping & Chang, Yufang & Yan, Huaicheng, 2025. "Addressing intermittency in medium-term photovoltaic and wind power forecasting using a hybrid xLSTM-TCCNN model with numerical weather predictions," Renewable Energy, Elsevier, vol. 253(C).
    4. Wang, Tao & Xu, Ye & Qin, Yu & Wang, Xu & Zheng, Feifan & Li, Wei, 2025. "Short-term PV forecasting of multiple scenarios based on multi-dimensional clustering and hybrid transformer-BiLSTM with ECPO," Energy, Elsevier, vol. 334(C).
    5. Yu, Sheng & He, Bin & Fang, Lei, 2025. "Multi-step short-term forecasting of photovoltaic power utilizing TimesNet with enhanced feature extraction and a novel loss function," Applied Energy, Elsevier, vol. 388(C).
    6. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe & Ruan, Yingjun, 2025. "A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting," Applied Energy, Elsevier, vol. 378(PA).
    7. 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.
    8. Wang, Keqi & Wang, Lijie & Meng, Qiang & Yang, Chao & Lin, Yangshu & Zhu, Junye & Zhao, Zhongyang & Zhou, Can & Zheng, Chenghang & Gao, Xiang, 2025. "Accurate photovoltaic power prediction via temperature correction with physics-informed neural networks," Energy, Elsevier, vol. 328(C).
    9. Guo, Xiaojie & Zeng, Pingliang & Xiong, Xiong & Wang, Guodong, 2026. "A physical model and data-driven synergy method for short-term photovoltaic power prediction," Renewable Energy, Elsevier, vol. 256(PE).
    10. Lakhdar Nadjib Boucetta & Youssouf Amrane & Aissa Chouder & Saliha Arezki & Sofiane Kichou, 2024. "Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model," Energies, MDPI, vol. 17(7), pages 1-21, April.
    11. Sun, Yang & Tian, Zhirui, 2025. "Solving few-shot problem in wind speed prediction: A novel transfer strategy based on decomposition and learning ensemble," Applied Energy, Elsevier, vol. 377(PD).
    12. Li, Jiaqian & Rao, Congjun & Gao, Mingyun & Xiao, Xinping & Goh, Mark, 2025. "Efficient calculation of distributed photovoltaic power generation power prediction via deep learning," Renewable Energy, Elsevier, vol. 246(C).
    13. Zhang, Mingyue & Han, Yang & Wang, Chaoyang & Yang, Ping & Wang, Congling & Zalhaf, Amr S., 2024. "Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data," Applied Energy, Elsevier, vol. 375(C).
    14. Kim, Jimin & Obregon, Josue & Park, Hoonseok & Jung, Jae-Yoon, 2024. "Multi-step photovoltaic power forecasting using transformer and recurrent neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    15. Chen, Jie & Peng, Tian & Qian, Shijie & Ge, Yida & Wang, Zheng & Nazir, Muhammad Shahzad & Zhang, Chu, 2025. "An error-corrected deep Autoformer model via Bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction," Applied Energy, Elsevier, vol. 377(PD).
    16. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    17. Deng, Feng & Wang, Tianhang & Tao, Wanting & Darkwa, Jo & Li, Yilin, 2025. "A LSTM-model based approach for long-term forecasting of high-rise residential building integrated photovoltaic system," Energy, Elsevier, vol. 338(C).
    18. Sameer Al-Dahidi & Manoharan Madhiarasan & Loiy Al-Ghussain & Ahmad M. Abubaker & Adnan Darwish Ahmad & Mohammad Alrbai & Mohammadreza Aghaei & Hussein Alahmer & Ali Alahmer & Piero Baraldi & Enrico Z, 2024. "Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework," Energies, MDPI, vol. 17(16), pages 1-38, August.
    19. Li, Yifan & Liu, Gang & Cao, Yisheng & Chen, Jiawei & Gang, Xiao & Tang, Jianchao, 2025. "WNPS-LSTM-Informer: A Hybrid Stacking model for medium-term photovoltaic power forecasting with ranked feature selection," Renewable Energy, Elsevier, vol. 244(C).
    20. Wang, Yongli & Gao, Mingchen & Wang, Jingyan & Wang, Shuo & Liu, Yang & Zhu, Jinrong & Tan, Zhongfu, 2021. "Measurement and key influencing factors of the economic benefits for China’s photovoltaic power generation: A LCOE-based hybrid model," Renewable Energy, Elsevier, vol. 169(C), pages 935-952.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jeners:v:17:y:2024:i:17:p:4434-:d:1471076. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.