IDEAS home Printed from https://ideas.repec.org/a/ovi/oviste/vxxiiy2022i1p117-123.html
   My bibliography  Save this article

Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management

Author

Listed:
  • Simona-Vasilica Oprea

    (Bucharest University of Economic Studies, Romania)

  • Adela Bâra

    (Bucharest University of Economic Studies, Romania)

Abstract

The actual context characterized by the high prices of the conventional power gives more and more credit to the Renewable Energy Sources (RES) to cover load requirements in large amounts. However, the volatility of RES (especially solar and wind) restricts their smooth integration into the residential consumption energy mix. One of the main challenges is to maximize the consumption of appliances from RES taking into account their availability. To fulfil this objective, first, a performant forecast is necessary to create the day-ahead schedule and optimize the operation of appliances. In this paper, we propose a framework to perform PV forecast with machine learning algorithms and various data sources for the energy management of the off-grid prosumers.

Suggested Citation

  • Simona-Vasilica Oprea & Adela Bâra, 2022. "Machine Learning Algorithms and PV Forecast for Off-Grid Prosumers Energy Management," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 117-123, September.
    • Handle: RePEc:ovi:oviste:v:xxii:y:2022:i:1:p:117-123
    as

    Download full text from publisher

    File URL: https://stec.univ-ovidius.ro/html/anale/RO/2022-2/Section%201%20and%202/15.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    renewable energy sources (RES); maximizing consumption from RES; day-ahead forecast; machine learning; prosumers;
    All these keywords.

    JEL classification:

    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q29 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Other

    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:ovi:oviste:v:xxii:y:2022:i:1:p:117-123. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Gheorghiu Gabriela (email available below). General contact details of provider: https://edirc.repec.org/data/feoviro.html .

    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.