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Multi-region electricity demand prediction with ensemble deep neural networks

Author

Listed:
  • Muhammad Irfan
  • Ahmad Shaf
  • Tariq Ali
  • Mariam Zafar
  • Saifur Rahman
  • Salim Nasar Faraj Mursal
  • Faisal AlThobiani
  • Majid A. Almas
  • H M Attar
  • Nagi Abdussamiee

Abstract

Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption.

Suggested Citation

  • Muhammad Irfan & Ahmad Shaf & Tariq Ali & Mariam Zafar & Saifur Rahman & Salim Nasar Faraj Mursal & Faisal AlThobiani & Majid A. Almas & H M Attar & Nagi Abdussamiee, 2023. "Multi-region electricity demand prediction with ensemble deep neural networks," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-23, May.
    • Handle: RePEc:plo:pone00:0285456
    DOI: 10.1371/journal.pone.0285456
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    References listed on IDEAS

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