IDEAS home Printed from https://ideas.repec.org/a/igg/jskd00/v13y2021i4p32-49.html
   My bibliography  Save this article

Design of a Fuzzy Logic Controller for Short-Term Load Forecasting With Randomly Varying Load

Author

Listed:
  • D. V. N. Ananth

    (Raghu Institute of Technology, Modavalasa, India)

  • Lagudu Venkata Suresh Kumar

    (GMR Institute of Technology, India)

  • Tulasichandra Sekhar Gorripotu

    (Sri Sivani College of Engineering, India)

  • Ahmad Taher Azar

    (College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt)

Abstract

Short-term load forecasting (STLF) is an integral component of energy management systems. In this paper, fuzzy logic-based algorithm is used for short-term load forecasting. The load changes over time and the goal is to satisfy the shift in demand and to maintain a fault as low as possible between the reference and real powers. The error in the load demand in mega-watt (MW) is compared with proposed technique as well as conventional methods. Three cases were investigated in which the load changes were 1) more random in nature, but the variance to the reference was more; 2) the random load changes were simpler, but a little different from the reference; and lastly, 3) the load changing was random, and the reference deviation was maximum. The results are analyzed for different load changes, and the corresponding results are verified using MATLAB. The deviation of the error value in load response is less experienced with a fuzzy logic controller than with a traditional system, and in fewer iterations, the objective function is also achieved.

Suggested Citation

  • D. V. N. Ananth & Lagudu Venkata Suresh Kumar & Tulasichandra Sekhar Gorripotu & Ahmad Taher Azar, 2021. "Design of a Fuzzy Logic Controller for Short-Term Load Forecasting With Randomly Varying Load," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 13(4), pages 32-49, October.
  • Handle: RePEc:igg:jskd00:v:13:y:2021:i:4:p:32-49
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSKD.2021100103
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. S. Nivetha & H. Hannah Inbarani, 2023. "Novel Hybrid Genetic Arithmetic Optimization for Feature Selection and Classification of Pulmonary Disease Images," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 15(1), pages 1-58, January.
    2. S. Nivetha & H. Hannah Inbarani, 2023. "Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images," International Journal of Sociotechnology and Knowledge Development (IJSKD), IGI Global, vol. 15(1), pages 1-35, January.

    More about this item

    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:igg:jskd00:v:13:y:2021:i:4:p:32-49. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.