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Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq

Author

Listed:
  • Ahmed Mazin Majid AL-Qaysi

    (Department of Electrical Engineering, Yildiz Technical University, 34220 Istanbul, Turkey)

  • Altug Bozkurt

    (Department of Electrical Engineering, Yildiz Technical University, 34220 Istanbul, Turkey)

  • Yavuz Ates

    (Department of Electrical Electronics Engineering, Manisa Celal Bayar University, 45140 Manisa, Turkey)

Abstract

This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN–GA model has good accuracy and low error rates for load predictions compared to other models, resulting in the best performance for our system.

Suggested Citation

  • Ahmed Mazin Majid AL-Qaysi & Altug Bozkurt & Yavuz Ates, 2023. "Load Forecasting Based on Genetic Algorithm–Artificial Neural Network-Adaptive Neuro-Fuzzy Inference Systems: A Case Study in Iraq," Energies, MDPI, vol. 16(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2919-:d:1104238
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    References listed on IDEAS

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    1. Athanasios Ioannis Arvanitidis & Dimitrios Bargiotas & Aspassia Daskalopulu & Vasileios M. Laitsos & Lefteri H. Tsoukalas, 2021. "Enhanced Short-Term Load Forecasting Using Artificial Neural Networks," Energies, MDPI, vol. 14(22), pages 1-14, November.
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    Cited by:

    1. Sergiu-Mihai Hategan & Nicoleta Stefu & Marius Paulescu, 2023. "Calibration of GFS Solar Irradiation Forecasts: A Case Study in Romania," Energies, MDPI, vol. 16(11), pages 1-11, May.
    2. Fangzong Wang & Zuhaib Nishtar, 2024. "Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach," Energies, MDPI, vol. 17(11), pages 1-24, May.

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