IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v251y2025ics0960148125009875.html
   My bibliography  Save this article

Energy generation in a multi-sided photovoltaic system: Analysis of the prediction effectiveness

Author

Listed:
  • Zator, Sławomir
  • Osuchowski, Jakub
  • Kasana, Singara Singh
  • Shelke, Nitin Arvind
  • Tomaszewski, Michał

Abstract

This article investigates the role of multi-oriented photovoltaic (PV) systems in enhancing energy self-sufficiency and reducing greenhouse gas emissions. Focusing on the evaluation of energy production prediction performance, the study utilizes data from four multi-oriented PV installations from 2022, standardized to 1 kWp capacity for comparative analysis. A custom-built measurement system, incorporating single-phase and three-phase meters, and solar irradiance data from the Solcast API, facilitated the creation of a dataset for predictive modeling. The Random Forest machine learning algorithm was employed to develop predictive models, enabling a comprehensive analysis of different installation orientations, including feature importance, prediction errors, and self-consumption and self-sufficiency rates. The study introduces new indicators alongside typical metrics to assess the accuracy of energy production predictions, aiming to address the surplus or deficit in energy forecasting. The results offer valuable insights into optimizing energy generation and grid integration of PV systems, highlighting the potential for improved prediction reliability and precision in multi-oriented installations.

Suggested Citation

  • Zator, Sławomir & Osuchowski, Jakub & Kasana, Singara Singh & Shelke, Nitin Arvind & Tomaszewski, Michał, 2025. "Energy generation in a multi-sided photovoltaic system: Analysis of the prediction effectiveness," Renewable Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:renene:v:251:y:2025:i:c:s0960148125009875
    DOI: 10.1016/j.renene.2025.123325
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125009875
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.123325?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 search for a different version of it.

    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:251:y:2025:i:c:s0960148125009875. 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: 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.