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Energy load forecasting model based on deep neural networks for smart grids

Author

Listed:
  • Faisal Mohammad

    (Chonbuk National University)

  • Young-Chon Kim

    (Chonbuk National University)

Abstract

In recent, smart grid has emerged as a promising technology to facilitate the future electric power grid and to balance between supply and demand. However, the intermittent nature of distributed energy resources causes dynamic uncertainties and nonlinearity in the smart grid environment. This may result in a large stress on power grid and has a big influence on energy planning, especially the generation and distribution. Therefore, energy load forecasting plays an important role in facilitating the operation of the future smart grid. Using the traditional statistical and machine learning approach there exists a significant forecasting error and high degree of overfitting. In this paper, we propose an energy load forecasting (ELF) model based on deep neural network architectures to manage the energy consumption in smart grids. First we investigate the applicability of two deep neural network architectures: deep feed-forward neural network (deep-FNN) and deep recurrent neural network (deep-RNN). To evaluate the models with low error, we simulate both architectures with multi size training set. Further, various activation functions and different combinations of hidden layer architectures are also tested. The simulation results are compared in terms of mean absolute percentage error. The results show that the proposed ELF model has attained better generalization and outperform the existing load forecasting models based on the shallow neural network, ensemble tree bagger and generalized linear regression.

Suggested Citation

  • Faisal Mohammad & Young-Chon Kim, 2020. "Energy load forecasting model based on deep neural networks for smart grids," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(4), pages 824-834, August.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:4:d:10.1007_s13198-019-00884-9
    DOI: 10.1007/s13198-019-00884-9
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    References listed on IDEAS

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    1. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    2. Joanna Nowicka-Zagrajek & Rafal Weron, 2002. "Modeling electricity loads in California: ARMA models with hyperbolic noise," HSC Research Reports HSC/02/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
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    Cited by:

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    3. Mamoona Zahid & Farhat Iqbal & Dimitrios Koutmos, 2022. "Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning," Risks, MDPI, vol. 10(12), pages 1-18, December.

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