IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i5p1073-d1344808.html

Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations

Author

Listed:
  • Pavol Belany

    (Research Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia)

  • Peter Hrabovsky

    (Research Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia)

  • Zuzana Florkova

    (Research Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia)

Abstract

In recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize this objective, it is crucial to quantify the probability of achieving the desired state—production exceeding consumption. This article deals with the computation of the probability that the energy required to charge an electric vehicle will originate from a renewable source at a specific time and for a predetermined charging duration. The base of the model lies in artificial neural networks, which serve as an ancillary tool for the actual probability assessment. Neural networks are used to forecast the values of energy production and consumption. Following the processing of these data, the probability of energy availability for a given day and month is determined. A total of seven scenarios are calculated, representing individual days of the week. These findings can help users in their decision-making process regarding when and for how long to connect their electric vehicle to a charging station to receive assured clean energy from a local photovoltaic source.

Suggested Citation

  • Pavol Belany & Peter Hrabovsky & Zuzana Florkova, 2024. "Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations," Energies, MDPI, vol. 17(5), pages 1-34, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1073-:d:1344808
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/5/1073/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/5/1073/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(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. Al-Dahidi, Sameer & Alrbai, Mohammad & Rinchi, Bilal & Alahmer, Hussein & Al-Ghussain, Loiy & Hayajneh, Hassan S. & Alahmer, Ali, 2025. "Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models," Energy, Elsevier, vol. 323(C).
    2. Su, Kehan & Yang, Chao & Shao, Yuhao & Jiang, Dalin & Zhou, Can & Wang, Lijie & Liu, Dazheng & Zhu, Peiqi & Ding, Yi & Zheng, Chenghang & Gao, Xiang, 2025. "Accelerating multi-energy system online optimization via integer state variable prediction with operation strategy learning," Energy, Elsevier, vol. 341(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. Zhou, Hai & Yang, Fan & Wu, Ji & Hu, Siyu & Ma, Wenwen & Ju, Rongrong, 2024. "Comprehensive evaluation methods for photovoltaic output anomalies based on weather classification," Renewable Energy, Elsevier, vol. 231(C).
    5. Oscar Trull & Juan Carlos García-Díaz & Angel Peiró-Signes, 2025. "A Comparative Study of Statistical and Machine Learning Methods for Solar Irradiance Forecasting Using the Folsom PLC Dataset," Energies, MDPI, vol. 18(15), pages 1-19, August.
    6. Wang, Yongli & Wang, Huan & Meng, Xiao & Dong, Huanran & Chen, Xin & Xiang, Hao & Xing, Juntai, 2023. "Considering the dual endogenous-exogenous uncertainty integrated energy multiple load short-term forecast," Energy, Elsevier, vol. 285(C).
    7. Zhang, Qi & Yang, Kexin & Fang, Siying, 2025. "Stochastic optimization of electric vehicle charging strategy based on day-ahead high precision forecast for renewable power and charging demand," Energy, Elsevier, vol. 338(C).
    8. 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).
    9. Taorong Jia & Guoqing Yang & Lixiao Yao, 2024. "The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization," Energies, MDPI, vol. 17(19), pages 1-20, October.
    10. Jinhwa Jeong & Dongkyu Lee & Young Tae Chae, 2023. "A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service," Energies, MDPI, vol. 16(22), pages 1-21, November.
    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. Chenhao, Sun & Yaoding, Wang & Xiangjun, Zeng & Wen, Wang & Chun, Chen & Yang, Shen & Zhijie, Lian & Quan, Zhou, 2024. "A hybrid spatiotemporal distribution forecast methodology for IES vulnerabilities under uncertain and imprecise space-air-ground monitoring data scenarios," Applied Energy, Elsevier, vol. 373(C).
    13. 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).
    14. Suh, Jangwon, 2025. "Economic analysis of a solar roof as an optional extra to electric vehicles in Korea: A case study," Renewable Energy, Elsevier, vol. 239(C).
    15. Yang, Mao & Guo, Zhenpeng & Wang, Da & Wang, Bo & Wang, Zhao & Huang, Tao, 2026. "Short-term photovoltaic power forecasting method considering historical information reuse and numerical weather forecasting," Renewable Energy, Elsevier, vol. 256(PB).

    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:5:p:1073-:d:1344808. 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.