IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v256y2026ipes0960148125019032.html

A physical model and data-driven synergy method for short-term photovoltaic power prediction

Author

Listed:
  • Guo, Xiaojie
  • Zeng, Pingliang
  • Xiong, Xiong
  • Wang, Guodong

Abstract

This article aims to enhance the accuracy of short-term photovoltaic power forecasts by introducing a novel hybrid forecasting method known as the Numerical Weather Prediction Radiation Revision (NRR), Clear Sky Radiation Estimation (CRE), and a tree-based power predictor, collectively referred to as the NETA method. To evaluate the performance of the NETA method, four machine learning algorithms Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were applied across various test periods. Experimental results demonstrate the consistent superiority of the NETA method over the alternatives, evidenced by average reductions of 28.06% in rMAE, 23.47% in rRMSE, and an average increase of 11.9% in R2. The analysis confirms the effectiveness of the NETA method in forecasting short-term photovoltaic power.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:renene:v:256:y:2026:i:pe:s0960148125019032
    DOI: 10.1016/j.renene.2025.124239
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125019032
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.124239?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Yolcan, Oguz Ozan, 2023. "World energy outlook and state of renewable energy: 10-Year evaluation," Innovation and Green Development, Elsevier, vol. 2(4).
    2. Antonanzas-Torres, F. & Urraca, R. & Polo, J. & Perpiñán-Lamigueiro, O. & Escobar, R., 2019. "Clear sky solar irradiance models: A review of seventy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 107(C), pages 374-387.
    3. Nunes Maciel, Joylan & Javier Gimenez Ledesma, Jorge & Hideo Ando Junior, Oswaldo, 2024. "Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    4. 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.
    5. Tahir, Muhammad Faizan & Yousaf, Muhammad Zain & Tzes, Anthony & El Moursi, Mohamed Shawky & El-Fouly, Tarek H.M., 2024. "Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
    6. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    7. Cao, Yisheng & Liu, Gang & Luo, Donghua & Bavirisetti, Durga Prasad & Xiao, Gang, 2023. "Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model," Energy, Elsevier, vol. 283(C).
    8. Mo, Fan & Jiao, Xuan & Li, Xingshuo & Du, Yang & Yao, Yunting & Meng, Yuxiang & Ding, Shuye, 2024. "A novel multi-step ahead solar power prediction scheme by deep learning on transformer structure," Renewable Energy, Elsevier, vol. 230(C).
    9. Mellit, A. & Pavan, A. Massi & Lughi, V., 2021. "Deep learning neural networks for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 172(C), pages 276-288.
    10. Dao, Phong B. & Barszcz, Tomasz & Staszewski, Wieslaw J., 2024. "Anomaly detection of wind turbines based on stationarity analysis of SCADA data," Renewable Energy, Elsevier, vol. 232(C).
    11. Zheng, Jianqin & Du, Jian & Wang, Bohong & Klemeš, Jiří Jaromír & Liao, Qi & Liang, Yongtu, 2023. "A hybrid framework for forecasting power generation of multiple renewable energy sources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    12. 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).
    13. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    14. Liu, Wencheng & Mao, Zhizhong, 2024. "Short-term photovoltaic power forecasting with feature extraction and attention mechanisms," Renewable Energy, Elsevier, vol. 226(C).
    15. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).
    16. Prósper, Miguel A. & Otero-Casal, Carlos & Fernández, Felipe Canoura & Miguez-Macho, Gonzalo, 2019. "Wind power forecasting for a real onshore wind farm on complex terrain using WRF high resolution simulations," Renewable Energy, Elsevier, vol. 135(C), pages 674-686.
    17. Zhou, Wenqian & Li, Xiangli & Duanmu, Lin & Yuan, Chao, 2024. "Investigation on rooftop PV performance and impact on microclimate in tropical cities ---- A WRF modelling study in Singapore," Renewable Energy, Elsevier, vol. 237(PB).
    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. Shi, Chaojun & Xie, Xiongbin & Zhang, Ke & Zhang, Xiaoyun & Su, Zibo & Xiao, Junchi, 2026. "CloudPVNet: A fine-grained ground-based cloud image segmentation method for photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 258(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. 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).
    2. 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).
    3. Deng, Ruizhe & Wang, Yiming & Xu, Po & Luo, Futao & Chen, Qi & Zhang, Haoran & Chen, Yuntian & Zhang, Dongxiao, 2025. "A high-precision photovoltaic power forecasting model leveraging low-fidelity data through decoupled informer with multi-moment guidance," Renewable Energy, Elsevier, vol. 250(C).
    4. 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).
    5. Chen, Rujian & Liu, Gang & Cao, Yisheng & Xiao, Gang & Tang, Jianchao, 2024. "CGAformer: Multi-scale feature Transformer with MLP architecture for short-term photovoltaic power forecasting," Energy, Elsevier, vol. 312(C).
    6. 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).
    7. Yang, Shaomei & Luo, Yuman, 2025. "Short-term photovoltaic power prediction based on RF-SGMD-GWO-BiLSTM hybrid models," Energy, Elsevier, vol. 316(C).
    8. 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).
    9. Hao, Jianhua & Liu, Fangai & Zhang, Weiwei, 2024. "Multi-scale RWKV with 2-dimensional temporal convolutional network for short-term photovoltaic power forecasting," Energy, Elsevier, vol. 309(C).
    10. Wang, Min & Rao, Congjun & Xiao, Xinping & Hu, Zhuo & Goh, Mark, 2024. "Efficient shrinkage temporal convolutional network model for photovoltaic power prediction," Energy, Elsevier, vol. 297(C).
    11. 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).
    12. Gong, Jianqiang & Qu, Zhiguo & Zhu, Zhenle & Xu, Hongtao, 2025. "Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning," Energy, Elsevier, vol. 320(C).
    13. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).
    14. Zheng, Feifan & Li, Zhongyan & Xu, Ye & Li, Wei & Wang, Tao, 2026. "A hybrid prediction model of photovoltaic power system based on AP, ISSA-based VMD, CLKAN and error correction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PC).
    15. 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.
    16. Zhang, Ruoyang & Wu, Yu & Zhang, Lei & Xu, Chongbin & Wang, ZeYu & Zhang, Yanfeng & Sun, Xiaomin & Zuo, Xin & Wu, Yuhan & Chen, Qian, 2025. "A multiscale network with mixed features and extended regional weather forecasts for predicting short-term photovoltaic power," Energy, Elsevier, vol. 318(C).
    17. 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).
    18. 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).
    19. Yang, Yanru & Liu, Yu & Zhang, Yihang & Shu, Shaolong & Zheng, Junsheng, 2025. "DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting," Applied Energy, Elsevier, vol. 378(PA).
    20. Wang, Yong & Yang, Zhongsen & Zhou, Ying & Liu, Hao & Yang, Rui & Sun, Lang & Sapnken, Flavian Emmanuel & Narayanan, Govindasami, 2025. "A novel structure adaptive new information priority grey Bernoulli model and its application in China's renewable energy production," Renewable Energy, Elsevier, vol. 239(C).

    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:eee:renene:v:256:y:2026:i:pe:s0960148125019032. 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.