IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v256y2026ipas0960148125015423.html

A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine

Author

Listed:
  • Ridha, Hussein Mohammed
  • Ahmadipour, Masoud
  • Alghrairi, Mokhalad
  • Hizam, Hashim
  • Mirjalili, Seyedali
  • Zubaidi, Salah L.
  • Mohammed S, Marwa Y.

Abstract

The precise and accurate prediction of the photovoltaic (PV) system can ensure the grid-connected system's constant operation and defect-free performance. Nonetheless, the PV current system's chaotic output is compounded by unpredictable weather patterns, which lead to poor predict stability, accuracy, and increasing costs. This paper introduces a novel prediction hybrid model based on singular spectrum analysis (SSA), adaptive beluga whale optimization (ABWO), and an improved extreme learning machine (IELM). The SSA is first employed for pre-processing long-term data input and output-time series. Then, the ABWO algorithm is enhanced to increase the exploitation tendency, simplify whale fall mechanism, and improve exploration/exploitation balace. The ABWO is validated by comparing it to well-published methods using a range of benchmark functions and then implemented to address real-world engineering challenges. Additionally, the inverse matrix of the output weights is adjusted after the training phase to optimize the ELM model. Ultimately, the hyperparameters of the IELM model are optimized utilizing ABWO algorithm. The numerical experimental results demonstrated that the ABWO algorithm beat all other optimization methods and could solve the majority of the benchmark functions. In contrast, the proposed SSA-ABWO-IELM model showed exceptional performance when compared to other hybrid deep machine learning models, as evidenced by numerous statistical assessments.

Suggested Citation

  • Ridha, Hussein Mohammed & Ahmadipour, Masoud & Alghrairi, Mokhalad & Hizam, Hashim & Mirjalili, Seyedali & Zubaidi, Salah L. & Mohammed S, Marwa Y., 2026. "A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine," Renewable Energy, Elsevier, vol. 256(PA).
  • Handle: RePEc:eee:renene:v:256:y:2026:i:pa:s0960148125015423
    DOI: 10.1016/j.renene.2025.123878
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125015423
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2025.123878?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. Wang, Shinong & Wang, Zheng & Ge, Yuan & Amer, Ragab Ahmed, 2025. "Performance estimator of photovoltaic modules by integrating deep learning network with physical model," Energy, Elsevier, vol. 325(C).
    2. Liu, Yunfei & Liu, Yan & Cai, Hanhu & Zhang, Junran, 2023. "An innovative short-term multihorizon photovoltaic power output forecasting method based on variational mode decomposition and a capsule convolutional neural network," Applied Energy, Elsevier, vol. 343(C).
    3. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
    4. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    5. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    6. Ma, Huixin & Zhang, Chu & Peng, Tian & Nazir, Muhammad Shahzad & Li, Yiman, 2022. "An integrated framework of gated recurrent unit based on improved sine cosine algorithm for photovoltaic power forecasting," Energy, Elsevier, vol. 256(C).
    7. Lilin Cheng & Haixiang Zang & Tao Ding & Rong Sun & Miaomiao Wang & Zhinong Wei & Guoqiang Sun, 2018. "Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach," Energies, MDPI, vol. 11(8), pages 1-23, July.
    8. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    9. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    10. Gaviria, Jorge Felipe & Narváez, Gabriel & Guillen, Camilo & Giraldo, Luis Felipe & Bressan, Michael, 2022. "Machine learning in photovoltaic systems: A review," Renewable Energy, Elsevier, vol. 196(C), pages 298-318.
    11. Gawusu, Sidique & Zhang, Xiaobing & Yakubu, Sufyan & Debrah, Seth Kofi & Das, Oisik & Bundela, Nishant Singh, 2025. "Optimizing solar photovoltaic system performance: Insights and strategies for enhanced efficiency," Energy, Elsevier, vol. 319(C).
    12. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    13. Fu, Guoyin, 2018. "Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system," Energy, Elsevier, vol. 148(C), pages 269-282.
    14. 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).
    15. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    16. Chen, Zhicong & Yu, Hui & Luo, Linlu & Wu, Lijun & Zheng, Qiao & Wu, Zhenhui & Cheng, Shuying & Lin, Peijie, 2021. "Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves," Applied Energy, Elsevier, vol. 292(C).
    17. Tang, Huadu & Kang, Fei & Li, Xinyu & Sun, Yong, 2025. "Short-term photovoltaic power prediction model based on feature construction and improved transformer," Energy, Elsevier, vol. 320(C).
    18. He, Xinbo & Wang, Yong & Zhang, Yuyang & Ma, Xin & Wu, Wenqing & Zhang, Lei, 2022. "A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting," Applied Energy, Elsevier, vol. 325(C).
    19. Heydari, Azim & Astiaso Garcia, Davide & Keynia, Farshid & Bisegna, Fabio & De Santoli, Livio, 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    20. Li, Fengyun & Zheng, Haofeng & Li, Xingmei, 2022. "A novel hybrid model for multi-step ahead photovoltaic power prediction based on conditional time series generative adversarial networks," Renewable Energy, Elsevier, vol. 199(C), pages 560-586.
    21. Zhu, Jiebei & Li, Mingrui & Luo, Lin & Zhang, Bidan & Cui, Mingjian & Yu, Lujie, 2023. "Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction," Renewable Energy, Elsevier, vol. 208(C), pages 141-151.
    22. Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
    23. Xu, Shijie & Ma, Hui & Ekanayake, Chandima & Cui, Yi, 2025. "Swin transformer-based transferable PV forecasting for new PV sites with insufficient PV generation data," Renewable Energy, Elsevier, vol. 246(C).
    24. Ahmadipour, Masoud & Hizam, Hashim & Othman, Mohammad Lutfi & Radzi, Mohd Amran Mohd & Murthy, Avinash Srikanta, 2018. "Islanding detection technique using Slantlet Transform and Ridgelet Probabilistic Neural Network in grid-connected photovoltaic system," Applied Energy, Elsevier, vol. 231(C), pages 645-659.
    25. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    26. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    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. Yang, Shaomei & Luo, Yuman, 2025. "Short-term photovoltaic power prediction based on RF-SGMD-GWO-BiLSTM hybrid models," Energy, Elsevier, vol. 316(C).
    2. 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).
    3. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    4. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    5. 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).
    6. Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    7. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    8. 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).
    9. Zhu, Jianhua & He, Yaoyao, 2025. "A novel hybrid model based on evolving multi-quantile long and short-term memory neural network for ultra-short-term probabilistic forecasting of photovoltaic power," Applied Energy, Elsevier, vol. 377(PC).
    10. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    11. Li, Guozhu & Ding, Chenjun & Zhao, Naini & Wei, Jiaxing & Guo, Yang & Meng, Chong & Huang, Kailiang & Zhu, Rongxin, 2024. "Research on a novel photovoltaic power forecasting model based on parallel long and short-term time series network," Energy, Elsevier, vol. 293(C).
    12. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    13. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    14. 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).
    15. Jiahui Wang & Mingsheng Jia & Shishi Li & Kang Chen & Cheng Zhang & Xiuyu Song & Qianxi Zhang, 2024. "Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
    16. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    17. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
    18. 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).
    19. 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).
    20. Yuan An & Kaikai Dang & Xiaoyu Shi & Rong Jia & Kai Zhang & Qiang Huang, 2021. "A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period," Energies, MDPI, vol. 14(4), pages 1-18, February.

    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:pa:s0960148125015423. 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.