IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i16p2648-d1726779.html
   My bibliography  Save this article

Numerical Optimization of Neuro-Fuzzy Models Using Evolutionary Algorithms for Electricity Demand Forecasting in Pre-Tertiary Institutions

Author

Listed:
  • Stephen O. Oladipo

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0183, South Africa)

  • Udochukwu B. Akuru

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0183, South Africa)

  • Ogbonnaya I. Okoro

    (Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike 440101, Nigeria)

Abstract

Reliable electricity supply in educational facilities demands predictive frameworks that reflect usage patterns and consumption variability. This study investigates electricity-consumption forecasting in lower-to-middle-income pre-tertiary institutions in Western Cape, South Africa, using adaptive neuro-fuzzy inference systems (ANFISs) optimized by evolutionary algorithms. Using genetic algorithm (GA) and particle swarm optimization (PSO) algorithms, the impact of two clustering techniques, Subtractive Clustering (SC) and Fuzzy C-Means (FCM), along with their cogent hyperparameters, were investigated, yielding several sub-models. The efficacy of the proposed models was evaluated using five standard statistical metrics, while the optimal model was also compared with other variants and hybrid models. Results obtained showed that the GA-ANFIS-FCM with four clusters achieved the best performance, recording the lowest Root Mean Square Error (RMSE) of 3.83, Mean Absolute Error (MAE) of 2.40, Theil’s U of 0.87, and Standard Deviation (SD) of 3.82. The developed model contributes valuable insights towards informed energy decisions.

Suggested Citation

  • Stephen O. Oladipo & Udochukwu B. Akuru & Ogbonnaya I. Okoro, 2025. "Numerical Optimization of Neuro-Fuzzy Models Using Evolutionary Algorithms for Electricity Demand Forecasting in Pre-Tertiary Institutions," Mathematics, MDPI, vol. 13(16), pages 1-33, August.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:16:p:2648-:d:1726779
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/16/2648/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/16/2648/
    Download Restriction: no
    ---><---

    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:jmathe:v:13:y:2025:i:16:p:2648-:d:1726779. 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: 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.