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Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach

Author

Listed:
  • Mojtaba Ahmadieh Khanesar

    (Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

  • Jingyi Lu

    (Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

  • Thomas Smith

    (Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

  • David Branson

    (Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK)

Abstract

Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic system whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short-term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in the literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO.

Suggested Citation

  • Mojtaba Ahmadieh Khanesar & Jingyi Lu & Thomas Smith & David Branson, 2021. "Electrical Load Prediction Using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning Approach," Energies, MDPI, vol. 14(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3591-:d:576223
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    References listed on IDEAS

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    1. Mohammed M. Khalaf & Sayer Obaid Alharbi & Wathek Chammam, 2019. "Similarity Measures between Temporal Complex Intuitionistic Fuzzy Sets and Application in Pattern Recognition and Medical Diagnosis," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-16, July.
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    3. Wu, Jinran & Cui, Zhesen & Chen, Yanyan & Kong, Demeng & Wang, You-Gan, 2019. "A new hybrid model to predict the electrical load in five states of Australia," Energy, Elsevier, vol. 166(C), pages 598-609.
    4. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
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    Cited by:

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    2. Maher G. M. Abdolrasol & Mahammad Abdul Hannan & S. M. Suhail Hussain & Taha Selim Ustun & Mahidur R. Sarker & Pin Jern Ker, 2021. "Energy Management Scheduling for Microgrids in the Virtual Power Plant System Using Artificial Neural Networks," Energies, MDPI, vol. 14(20), pages 1-19, October.

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