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Applying Multi-Task Deep Learning Methods in Electricity Load Forecasting Using Meteorological Factors

Author

Listed:
  • Kai-Bin Huang

    (Department of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Tian-Shyug Lee

    (Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Jonathan Lee

    (Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA)

  • Jy-Ping Wu

    (Department of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Leemen Lee

    (Department of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan)

  • Hsiu-Mei Lee

    (Graduate Institute of Business Administration, Fu Jen Catholic University, New Taipei City 242, Taiwan)

Abstract

The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model’s performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals.

Suggested Citation

  • Kai-Bin Huang & Tian-Shyug Lee & Jonathan Lee & Jy-Ping Wu & Leemen Lee & Hsiu-Mei Lee, 2024. "Applying Multi-Task Deep Learning Methods in Electricity Load Forecasting Using Meteorological Factors," Mathematics, MDPI, vol. 12(20), pages 1-29, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3295-:d:1502883
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    References listed on IDEAS

    as
    1. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
    2. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
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