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Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview

Author

Listed:
  • Seyedeh Narjes Fallah

    (Independent Researcher, Sari 4816783787, Iran)

  • Mehdi Ganjkhani

    (Department of Electrical Engineering, Sharif University of Technology, Tehran P.O. Box 11365-11155, Iran)

  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Kwok-wing Chau

    (Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China)

Abstract

Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed.

Suggested Citation

  • Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:393-:d:201101
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    References listed on IDEAS

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    Cited by:

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    5. Dávid Csercsik & Ádám Sleisz & Péter Márk Sőrés, 2019. "The Uncertain Bidder Pays Principle and Its Implementation in a Simple Integrated Portfolio-Bidding Energy-Reserve Market Model," Energies, MDPI, vol. 12(15), pages 1-25, August.
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    7. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
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    11. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    12. Laouafi, Abderrezak & Laouafi, Farida & Boukelia, Taqiy Eddine, 2022. "An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting," Applied Energy, Elsevier, vol. 322(C).
    13. Sizhe Zhang & Jinqi Liu & Jihong Wang, 2023. "High-Resolution Load Forecasting on Multiple Time Scales Using Long Short-Term Memory and Support Vector Machine," Energies, MDPI, vol. 16(4), pages 1-22, February.
    14. Lalitpat Aswanuwath & Warut Pannakkong & Jirachai Buddhakulsomsiri & Jessada Karnjana & Van-Nam Huynh, 2023. "A Hybrid Model of VMD-EMD-FFT, Similar Days Selection Method, Stepwise Regression, and Artificial Neural Network for Daily Electricity Peak Load Forecasting," Energies, MDPI, vol. 16(4), pages 1-24, February.
    15. Songtao Huang & Jun Shen & Qingquan Lv & Qingguo Zhou & Binbin Yong, 2022. "A Novel NODE Approach Combined with LSTM for Short-Term Electricity Load Forecasting," Future Internet, MDPI, vol. 15(1), pages 1-20, December.
    16. Rae-Jun Park & Kyung-Bin Song & Bo-Sung Kwon, 2020. "Short-Term Load Forecasting Algorithm Using a Similar Day Selection Method Based on Reinforcement Learning," Energies, MDPI, vol. 13(10), pages 1-19, May.
    17. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.
    18. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Mohmmed S. Adrees, 2021. "An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy," Sustainability, MDPI, vol. 13(11), pages 1-20, May.

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