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Short Term Energy Forecasting with Neural Networks

Author

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  • J. Stuart McMenamin
  • Frank A. Monforte

Abstract

Artificial neural networks are beginning to be used by electric utilities, to forecast hourly system loads on a day ahead basis. This paper discusses the neural network specification in terms of conventional econometric language, providing parallel concepts for terms such as training, learning, and nodes in the, hidden layer. It is shown that these models are flexible nonlinear equations that can be estimated using nonlinear least squares. It is argued that these models are especially well suited to hourly load forecasting, reflecting the presence of important nonlinearities and variable interactions. The paper proceeds to show how conventional statistics, such as the BIC and MAPE statistics can be used to select the number of nodes in the hidden layer. It is concluded that these models provide a powerful, robust and sensible approach to hourly load forecasting that will provide modest improvements in forecast accuracy relative to well-specified regression models.

Suggested Citation

  • J. Stuart McMenamin & Frank A. Monforte, 1998. "Short Term Energy Forecasting with Neural Networks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 43-61.
    • Handle: RePEc:aen:journl:1998v19-04-a02
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    Citations

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    Cited by:

    1. Hernández, Luis & Baladrón, Carlos & Aguiar, Javier M. & Carro, Belén & Sánchez-Esguevillas, Antonio & Lloret, Jaime, 2014. "Artificial neural networks for short-term load forecasting in microgrids environment," Energy, Elsevier, vol. 75(C), pages 252-264.
    2. Mostafa, Mohamed M. & Nataraajan, Rajan, 2009. "A neuro-computational intelligence analysis of the ecological footprint of nations," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3516-3531, July.
    3. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    4. Luis Hernandez & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio J. Sanchez-Esguevillas & Jaime Lloret, 2013. "Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks," Energies, MDPI, vol. 6(3), pages 1-24, March.
    5. Mostafa, Mohamed M. & El-Masry, Ahmed A., 2016. "Oil price forecasting using gene expression programming and artificial neural networks," Economic Modelling, Elsevier, vol. 54(C), pages 40-53.
    6. Nima Amjady & Farshid Keynia, 2011. "A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems," Energies, MDPI, vol. 4(3), pages 1-16, March.

    More about this item

    JEL classification:

    • F0 - International Economics - - General

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