IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i4p1033-d1072620.html
   My bibliography  Save this article

Photovoltaic Power Prediction Based on VMD-BRNN-TSP

Author

Listed:
  • Guici Chen

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan 430065, China
    College of Science, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Tingting Zhang

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan 430065, China
    College of Science, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Wenyu Qu

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan 430065, China
    College of Science, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Wenbo Wang

    (Hubei Province Key Laboratory of Systems Science in Metallurgical Process, Wuhan 430065, China
    College of Science, Wuhan University of Science and Technology, Wuhan 430065, China)

Abstract

Overfitting often occurs in neural network training, and neural networks with higher generalization ability are less prone to this phenomenon. Aiming at the problem that the generalization ability of photovoltaic (PV) power prediction model is insufficient, a PV power time-sharing prediction (TSP) model combining variational mode decomposition (VMD) and Bayesian regularization neural network (BRNN) is proposed. Firstly, the meteorological sequences related to the output power are selected by mutual information (MI) analysis. Secondly, VMD processing is performed on the filtered sequences, which is aimed at reducing the non-stationarity of the data; then, normalized cross-correlation (NCC) and signal-to-noise ratio (SNR) between the components obtained by signal decomposition and the original data are calculated, after which the key influencing factors are screened out to eliminate the correlation and redundancy of the data. Finally, the filtered meteorological sequences are divided into two datasets based on whether the irradiance of the day is zero or not. Meanwhile, the predictions are performed using BRNN for each of the two datasets. Then, the results are reordered in chronological order, and the prediction of PV power is realized conclusively. It was experimentally verified that the mean absolute value error ( M A E ) of the method proposed in this paper is 0.1281 , which is reduced by 40.28 % compared with the back propagation neural network (BPNN) model on the same dataset, the mean squared error ( M S E ) is 0.0962 , and the coefficient of determination ( R 2 ) is 0.9907 . Other error indicators also confirm that VMD is of much significance and TSP is contributive.

Suggested Citation

  • Guici Chen & Tingting Zhang & Wenyu Qu & Wenbo Wang, 2023. "Photovoltaic Power Prediction Based on VMD-BRNN-TSP," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:1033-:d:1072620
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/4/1033/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/4/1033/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lu Ye & Saadya Fahad Jabbar & Musaddak M. Abdul Zahra & Mou Leong Tan & Zaher Mundher Yaseen, 2021. "Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem," Complexity, Hindawi, vol. 2021, pages 1-14, April.
    2. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    3. Mohamed Mohana & Abdelaziz Salah Saidi & Salem Alelyani & Mohammed J. Alshayeb & Suhail Basha & Ali Eisa Anqi, 2021. "Small-Scale Solar Photovoltaic Power Prediction for Residential Load in Saudi Arabia Using Machine Learning," Energies, MDPI, vol. 14(20), pages 1-18, October.
    4. 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).
    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. Alexandru Matei & Stefan-Alexandru Precup & Dragos Circa & Arpad Gellert & Constantin-Bala Zamfirescu, 2023. "Estimating Travel Time for Autonomous Mobile Robots through Long Short-Term Memory," Mathematics, MDPI, vol. 11(7), pages 1-19, April.
    2. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    3. Junhao Wu & Yuan Hu & Daqing Wu & Zhengyong Yang, 2022. "An Aquatic Product Price Forecast Model Using VMD-IBES-LSTM Hybrid Approach," Agriculture, MDPI, vol. 12(8), pages 1-26, August.
    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. Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
    6. 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).
    7. Tian, Zhirui & Wang, Jiyang, 2023. "A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer," Renewable Energy, Elsevier, vol. 215(C).
    8. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    9. Beibei Hu & Yunhe Cheng, 2023. "Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction," Energies, MDPI, vol. 16(11), pages 1-22, May.
    10. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    11. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Masahiro Furukakoi & Paras Mandal & Tomonobu Senjyu, 2023. "Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty," Energies, MDPI, vol. 16(19), pages 1-25, September.
    12. Haider A. Khan & Shahryar Ghorbani & Elham Shabani & Shahab S. Band, 2024. "Enhancement of Neural Networks Model’s Predictions of Currencies Exchange Rates by Phase Space Reconstruction and Harris Hawks’ Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 835-860, February.
    13. Liu, Jingxuan & Zang, Haixiang & Zhang, Fengchun & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation," Renewable Energy, Elsevier, vol. 219(P1).
    14. 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).
    15. Xu, Shaozhen & Liu, Jun & Huang, Xiaoqiao & Li, Chengli & Chen, Zaiqing & Tai, Yonghang, 2024. "Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement," Renewable Energy, Elsevier, vol. 224(C).
    16. 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).
    17. Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).
    18. Wang, Junjie & Ye, Li & Ding, Xiaoyu & Dang, Yaoguo, 2024. "A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation," Energy, Elsevier, vol. 304(C).
    19. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
    20. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.

    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:jmathe:v:11:y:2023:i:4:p:1033-:d:1072620. 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.