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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
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    Cited by:

    1. 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.
    2. 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.

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