IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i15p5649-d1203881.html
   My bibliography  Save this article

Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group

Author

Listed:
  • Dengchang Ma

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China
    The College of Electrical Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou 310020, China)

  • Rongyi Xie

    (Guangxi Xijiang Group Investment Corp., Nanning 530000, China)

  • Guobing Pan

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China)

  • Zongxu Zuo

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China)

  • Lidong Chu

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China)

  • Jing Ouyang

    (Key Laboratory of E & M, Ministry of Education & Zhejiang Province, Zhejiang University of Technology, Hangzhou 310012, China)

Abstract

With the increasing proportion of distributed photovoltaic (DPV) installations in county-level power grids, to improve the centralized operation and maintenance of the stations and to meet the needs of power grid dispatching, the output of the county-level regional DPV stations group needs to be predicted. In this paper, the weather prediction information is used to predict the output based on the model input average strategy. To eliminate the effect of the selected non-optimal training sample collection period on the prediction accuracy, an ensemble prediction method based on the minimum redundancy maximum relevance criterion and TabNet model is carried out. To reduce the influence of weather prediction errors on the power output prediction, a modified model based on error prediction is proposed. The ensemble prediction model is used to predict the day-ahead output, and a combination prediction model based on the proposed ensemble prediction model and the proposed modified model is established to predict the hour-ahead output. The experimental results verify the effectiveness of the proposed models. Compared with the corresponding reference models, the proposed ensemble prediction method reduces the normalized mean absolute errors (nMAEs) and the normalized root mean square errors (nRMSEs) of the day-ahead output prediction results by 2.86% and 5.51%, respectively. The combination prediction model reduces the nMAE and nRMSE of the hour-ahead output prediction results by 3.05% and 3.05%, respectively. Therefore, the prediction accuracy can be improved by the proposed models.

Suggested Citation

  • Dengchang Ma & Rongyi Xie & Guobing Pan & Zongxu Zuo & Lidong Chu & Jing Ouyang, 2023. "Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group," Energies, MDPI, vol. 16(15), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5649-:d:1203881
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/15/5649/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/15/5649/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. da Silva Fonseca Junior, Joao Gari & Oozeki, Takashi & Ohtake, Hideaki & Shimose, Ken-ichi & Takashima, Takumi & Ogimoto, Kazuhiko, 2014. "Regional forecasts and smoothing effect of photovoltaic power generation in Japan: An approach with principal component analysis," Renewable Energy, Elsevier, vol. 68(C), pages 403-413.
    2. Fichter, Tobias & Soria, Rafael & Szklo, Alexandre & Schaeffer, Roberto & Lucena, Andre F.P., 2017. "Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model," Energy, Elsevier, vol. 121(C), pages 695-715.
    3. Lei Fu & Yiling Yang & Xiaolong Yao & Xufen Jiao & Tiantian Zhu, 2019. "A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion," Energies, MDPI, vol. 12(20), pages 1-23, October.
    4. Shaker, Hamid & Manfre, Daniel & Zareipour, Hamidreza, 2020. "Forecasting the aggregated output of a large fleet of small behind-the-meter solar photovoltaic sites," Renewable Energy, Elsevier, vol. 147(P1), pages 1861-1869.
    5. Koster, Daniel & Minette, Frank & Braun, Christian & O'Nagy, Oliver, 2019. "Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg," Renewable Energy, Elsevier, vol. 132(C), pages 455-470.
    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. Taeyoung Kim & Jinho Kim, 2021. "A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation," Energies, MDPI, vol. 14(14), pages 1-22, July.
    2. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(C).
    3. Pierro, Marco & Gentili, Damiano & Liolli, Fabio Romano & Cornaro, Cristina & Moser, David & Betti, Alessandro & Moschella, Michela & Collino, Elena & Ronzio, Dario & van der Meer, Dennis, 2022. "Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study," Renewable Energy, Elsevier, vol. 189(C), pages 983-996.
    4. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
    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. Gutiérrez-Alvarez, R. & Guerra, K. & Haro, P., 2023. "Market profitability of CSP-biomass hybrid power plants: Towards a firm supply of renewable energy," Applied Energy, Elsevier, vol. 335(C).
    7. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    8. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    9. Varone, Alberto & Heilmann, Zeno & Porruvecchio, Guido & Romanino, Alessandro, 2024. "Solar parking lot management: An IoT platform for smart charging EV fleets, using real-time data and production forecasts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    10. 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).
    11. Aqachmar, Zineb & Allouhi, Amine & Jamil, Abdelmajid & Gagouch, Belgacem & Kousksou, Tarik, 2019. "Parabolic trough solar thermal power plant Noor I in Morocco," Energy, Elsevier, vol. 178(C), pages 572-584.
    12. Ji-Won Cha & Sung-Kwan Joo, 2021. "Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)," Energies, MDPI, vol. 14(21), pages 1-19, October.
    13. Lai, Wenzhe & Zhen, Zhao & Wang, Fei & Fu, Wenjie & Wang, Junlong & Zhang, Xudong & Ren, Hui, 2024. "Sub-region division based short-term regional distributed PV power forecasting method considering spatio-temporal correlations," Energy, Elsevier, vol. 288(C).
    14. Miranda, Raul & Simoes, Sofia & Szklo, Alexandre & Schaeffer, Roberto, 2019. "Adding detailed transmission constraints to a long-term integrated assessment model – A case study for Brazil using the TIMES model," Energy, Elsevier, vol. 167(C), pages 791-803.
    15. Herrera, Milton M. & Dyner, Isaac & Cosenz, Federico, 2019. "Assessing the effect of transmission constraints on wind power expansion in northeast Brazil," Utilities Policy, Elsevier, vol. 59(C), pages 1-1.
    16. Gutiérrez, R.E. & Haro, P. & Gómez-Barea, A., 2021. "Techno-economic and operational assessment of concentrated solar power plants with a dual supporting system," Applied Energy, Elsevier, vol. 302(C).
    17. Medine Colak & Mehmet Yesilbudak & Ramazan Bayindir, 2020. "Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information," Energies, MDPI, vol. 13(4), pages 1-19, February.
    18. Sonja Simon & Tobias Naegler & Hans Christian Gils, 2018. "Transformation towards a Renewable Energy System in Brazil and Mexico—Technological and Structural Options for Latin America," Energies, MDPI, vol. 11(4), pages 1-26, April.
    19. Rafati, Amir & Joorabian, Mahmood & Mashhour, Elaheh & Shaker, Hamid Reza, 2021. "High dimensional very short-term solar power forecasting based on a data-driven heuristic method," Energy, Elsevier, vol. 219(C).
    20. Shivashankar, S. & Mekhilef, Saad & Mokhlis, Hazlie & Karimi, M., 2016. "Mitigating methods of power fluctuation of photovoltaic (PV) sources – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1170-1184.

    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:16:y:2023:i:15:p:5649-:d:1203881. 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.