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Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting

Author

Listed:
  • Miguel López

    (Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elx, Alacant, Spain)

  • Carlos Sans

    (Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elx, Alacant, Spain)

  • Sergio Valero

    (Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elx, Alacant, Spain)

  • Carolina Senabre

    (Department of Mechanic Engineering and Energy, Universidad Miguel Hernández, 03202 Elx, Alacant, Spain)

Abstract

Artificial Intelligence (AI) has been widely used in Short-Term Load Forecasting (STLF) in the last 20 years and it has partly displaced older time-series and statistical methods to a second row. However, the STLF problem is very particular and specific to each case and, while there are many papers about AI applications, there is little research determining which features of an STLF system is better suited for a specific data set. In many occasions both classical and modern methods coexist, providing combined forecasts that outperform the individual ones. This paper presents a thorough empirical comparison between Neural Networks (NN) and Autoregressive (AR) models as forecasting engines. The objective of this paper is to determine the circumstances under which each model shows a better performance. It analyzes one of the models currently in use at the National Transport System Operator in Spain, Red Eléctrica de España (REE), which combines both techniques. The parameters that are tested are the availability of historical data, the treatment of exogenous variables, the training frequency and the configuration of the model. The performance of each model is measured as RMSE over a one-year period and analyzed under several factors like special days or extreme temperatures. The AR model has 0.13% lower error than the NN under ideal conditions. However, the NN model performs more accurately under certain stress situations.

Suggested Citation

  • Miguel López & Carlos Sans & Sergio Valero & Carolina Senabre, 2018. "Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting," Energies, MDPI, vol. 11(8), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2080-:d:163007
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    References listed on IDEAS

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    Cited by:

    1. Manogaran Madhiarasan & Mohamed Louzazni, 2021. "Different Forecasting Horizons Based Performance Analysis of Electricity Load Forecasting Using Multilayer Perceptron Neural Network," Forecasting, MDPI, vol. 3(4), pages 1-35, November.
    2. Yijun Wang & Peiqian Guo & Nan Ma & Guowei Liu, 2022. "Robust Wavelet Transform Neural-Network-Based Short-Term Load Forecasting for Power Distribution Networks," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    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.
    4. Gabriel Trierweiler Ribeiro & João Guilherme Sauer & Naylene Fraccanabbia & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting," Energies, MDPI, vol. 13(9), pages 1-19, May.
    5. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Automatic Selection of Temperature Variables for Short-Term Load Forecasting," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    6. Alfredo Candela Esclapez & Miguel López García & Sergio Valero Verdú & Carolina Senabre Blanes, 2022. "Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting," Energies, MDPI, vol. 15(10), pages 1-18, May.

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