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Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms

Author

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  • Mobarak Abumohsen

    (Department of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine)

  • Amani Yousef Owda

    (Department of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine)

  • Majdi Owda

    (Faculty of Data Science, Arab American University, Ramallah P600, Palestine)

Abstract

Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), and (3) Recurrent Neural Networks (RNN). The models were tested, and the GRU model achieved the best performance in terms of accuracy and the lowest error. Results show that the GRU model achieved an R-squared of 90.228%, Mean Square Error (MSE) of 0.00215, and Mean Absolute Error (MAE) of 0.03266.

Suggested Citation

  • Mobarak Abumohsen & Amani Yousef Owda & Majdi Owda, 2023. "Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms," Energies, MDPI, vol. 16(5), pages 1-31, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2283-:d:1082006
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    References listed on IDEAS

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    Cited by:

    1. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
    2. Xinjian Xiang & Tianshun Yuan & Guangke Cao & Yongping Zheng, 2024. "Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm," Energies, MDPI, vol. 17(8), pages 1-21, April.
    3. Aydin Zaboli & Swetha Rani Kasimalla & Kuchan Park & Younggi Hong & Junho Hong, 2024. "A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies," Energies, MDPI, vol. 17(11), pages 1-27, May.
    4. Zhuoqun Zou & Jing Wang & Ning E & Can Zhang & Zhaocai Wang & Enyu Jiang, 2023. "Short-Term Power Load Forecasting: An Integrated Approach Utilizing Variational Mode Decomposition and TCN–BiGRU," Energies, MDPI, vol. 16(18), pages 1-17, September.
    5. Serrano-Arévalo, Tania Itzel & López-Flores, Francisco Javier & Raya-Tapia, Alma Yunuen & Ramírez-Márquez, César & Ponce-Ortega, José María, 2023. "Optimal expansion for a clean power sector transition in Mexico based on predicted electricity demand using deep learning scheme," Applied Energy, Elsevier, vol. 348(C).
    6. Serdal Atiç & Ercan Izgi, 2024. "Smart Reserve Planning Using Machine Learning Methods in Power Systems with Renewable Energy Sources," Sustainability, MDPI, vol. 16(12), pages 1-21, June.
    7. Tulin Guzel & Hakan Cinar & Mehmet Nabi Cenet & Kamil Doruk Oguz & Ahmet Yucekaya & Mustafa Hekimoglu, 2023. "A Framework to Forecast Electricity Consumption of Meters using Automated Ranking and Data Preprocessing," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 179-193, September.
    8. George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos & Constantinos Hilas, 2023. "Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network," Energies, MDPI, vol. 16(10), pages 1-20, May.

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