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A New Short Term Electrical Load Forecasting by Type-2 Fuzzy Neural Networks

Author

Listed:
  • Man-Wen Tian

    (National Key Project Laboratory, Jiangxi University of Engineering, Xinyu 338000, China)

  • Khalid Alattas

    (Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 22254, Saudi Arabia)

  • Fayez El-Sousy

    (Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia)

  • Abdullah Alanazi

    (Department of Chemistry, Faculty of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Ardashir Mohammadzadeh

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
    School of Engineering and Technology, Duy Tan University, Da Nang 550000, Vietnam)

  • Jafar Tavoosi

    (Department of the Electronic System, Aalborg University, 9220 Aalborg, Denmark)

  • Saleh Mobayen

    (Future Technology Research Center, National Yunlin University of Science and Technology, Douliu 64002, Taiwan)

  • Paweł Skruch

    (Department of Automatic Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

Abstract

In this study, we present a new approach for load forecasting (LF) using a recurrent fuzzy neural network (RFNN) for Kermanshah City. Imagine if there is a need for electricity in a region in the coming years, we will have to build a power plant or reinforce transmission lines, so this will be resolved if accurate forecasts are made at the right time. Furthermore, suppose that by building distributed generation plants, and predicting future consumption, we can conclude that production will be more than consumption, so we will seek to export energy to other countries and make decisions on this. In this paper, a novel combination of neural networks (NNs) and type-2 fuzzy systems (T2FSs) is used for load forecasting. Adding feedback to the fuzzy neural network can also benefit from past moments. This feedback structure is called a recurrent fuzzy neural network. In this paper, Kermanshah urban electrical load data is used. The simulation results prove the efficiency of this method for forecasting the electrical load. We found that we can accurately predict the electrical load of the city for the next day with 98% accuracy. The accuracy index is the evaluation of mean absolute percentage error (MAPE). The main contributions are: (1) Introducing a new fuzzy neural network. (2) Improving and increasing the accuracy of forecasting using the proposed fuzzy neural network. (3) Taking data from a specific area (Kermanshah City) and forecasting the electrical load for that area. (4) The ability to enter new data without calculations from the beginning.

Suggested Citation

  • Man-Wen Tian & Khalid Alattas & Fayez El-Sousy & Abdullah Alanazi & Ardashir Mohammadzadeh & Jafar Tavoosi & Saleh Mobayen & Paweł Skruch, 2022. "A New Short Term Electrical Load Forecasting by Type-2 Fuzzy Neural Networks," Energies, MDPI, vol. 15(9), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3034-:d:798541
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    References listed on IDEAS

    as
    1. Mengran Zhou & Tianyu Hu & Kai Bian & Wenhao Lai & Feng Hu & Oumaima Hamrani & Ziwei Zhu, 2021. "Short-Term Electric Load Forecasting Based on Variational Mode Decomposition and Grey Wolf Optimization," Energies, MDPI, vol. 14(16), pages 1-17, August.
    2. Janusz Sowinski, 2022. "Application of Real Options Approach to Analyse Economic Efficiency of Power Plant with CCS Installation under Uncertainty," Energies, MDPI, vol. 15(3), pages 1-17, January.
    3. Linfeng Lv & Juncheng Wang & Jiangqi Long, 2021. "Interval Type-2 Fuzzy Logic Anti-Lock Braking Control for Electric Vehicles under Complex Road Conditions," Sustainability, MDPI, vol. 13(20), pages 1-23, October.
    4. Janusz Sowinski, 2021. "The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company," Energies, MDPI, vol. 14(2), pages 1-18, January.
    5. Cristina Hora & Florin Ciprian Dan & Gabriel Bendea & Calin Secui, 2022. "Residential Short-Term Load Forecasting during Atypical Consumption Behavior," Energies, MDPI, vol. 15(1), pages 1-15, January.
    6. Grzegorz Dudek, 2021. "Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights," Energies, MDPI, vol. 14(11), pages 1-18, May.
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    Cited by:

    1. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.

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