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A Flexible Deep Learning Method for Energy Forecasting

Author

Listed:
  • Ihab Taleb

    (Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France
    These authors contributed equally to this work.)

  • Guillaume Guerard

    (Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France
    These authors contributed equally to this work.)

  • Frédéric Fauberteau

    (Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France
    These authors contributed equally to this work.)

  • Nga Nguyen

    (Research Center, Léonard de Vinci Pôle Universitaire, 92916 Paris La Défense, France
    These authors contributed equally to this work.)

Abstract

Load prediction with higher accuracy and less computing power has become an important problem in the smart grids domain in general and especially in demand-side management (DSM), as it can serve to minimize global warming and better integrate renewable energies. To this end, it is interesting to have a general prediction model which uses different standard machine learning models in order to be flexible enough to be used in different regions and/or countries and to give a prediction for multiple days or weeks with relatively good accuracy. Thus, we propose in this article a flexible hybrid machine learning model that can be used to make predictions of different ranges by using both standard neural networks and an automatic process of updating the weights of these models depending on their past errors. The model was tested on Mayotte Island and the mean absolute percentage error (MAPE) obtained was 1.71% for 30 min predictions, 3.5% for 24 h predictions, and 5.1% for one-week predictions.

Suggested Citation

  • Ihab Taleb & Guillaume Guerard & Frédéric Fauberteau & Nga Nguyen, 2022. "A Flexible Deep Learning Method for Energy Forecasting," Energies, MDPI, vol. 15(11), pages 1-16, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3926-:d:824551
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    References listed on IDEAS

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