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Intelligent Systems for Power Load Forecasting: A Study Review

Author

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  • Ibrahim Salem Jahan

    (ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic)

  • Vaclav Snasel

    (Computer Science Department, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic)

  • Stanislav Misak

    (ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic)

Abstract

The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study.

Suggested Citation

  • Ibrahim Salem Jahan & Vaclav Snasel & Stanislav Misak, 2020. "Intelligent Systems for Power Load Forecasting: A Study Review," Energies, MDPI, vol. 13(22), pages 1-12, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6105-:d:448942
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    References listed on IDEAS

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    2. Paweł Pijarski & Piotr Kacejko & Piotr Miller, 2023. "Advanced Optimisation and Forecasting Methods in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 16(6), pages 1-20, March.
    3. Umar Javed & Khalid Ijaz & Muhammad Jawad & Ejaz A. Ansari & Noman Shabbir & Lauri Kütt & Oleksandr Husev, 2021. "Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis," Energies, MDPI, vol. 14(17), pages 1-22, September.
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    6. Khawaja Haider Ali & Marvin Sigalo & Saptarshi Das & Enrico Anderlini & Asif Ali Tahir & Mohammad Abusara, 2021. "Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation," Energies, MDPI, vol. 14(18), pages 1-18, September.
    7. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    8. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
    9. Fujimoto, Yu & Fujita, Megumi & Hayashi, Yasuhiro, 2021. "Deep reservoir architecture for short-term residential load forecasting: An online learning scheme for edge computing," Applied Energy, Elsevier, vol. 298(C).
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