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Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System

Author

Listed:
  • John O’Donnell

    (DTE Electric, Detroit, MI 48226, USA
    Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Wencong Su

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

Abstract

Greater variation in electrical load should be expected in the future due to the increasing penetration of electric vehicles, photovoltaics, storage, and other technologies. The adoption of these technologies will vary by area and time, and if not identified early and managed by electric utilities, these new customer needs could result in power quality, reliability, and protection issues. Furthermore, comprehensively studying the uncertainty and variation in the load on circuit elements over periods of several months has the potential to increase the efficient use of traditional resources, non-wires alternatives, and microgrids to better serve customers. To increase the understanding of electrical load, the authors propose a multistep, attention-focused, and efficient machine learning process to provide probabilistic forecasts of distribution transformer load for several months into the future. The method uses the solar irradiance, temperature, dew point, time of day, and other features to achieve up to an 86% coefficient of determination (R 2 ).

Suggested Citation

  • John O’Donnell & Wencong Su, 2023. "Attention-Focused Machine Learning Method to Provide the Stochastic Load Forecasts Needed by Electric Utilities for the Evolving Electrical Distribution System," Energies, MDPI, vol. 16(15), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5661-:d:1204244
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    References listed on IDEAS

    as
    1. Majed A. Alotaibi, 2022. "Machine Learning Approach for Short-Term Load Forecasting Using Deep Neural Network," Energies, MDPI, vol. 15(17), pages 1-23, August.
    2. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
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    Cited by:

    1. John O’Donnell & Wencong Su, 2023. "A Stochastic Load Forecasting Approach to Prevent Transformer Failures and Power Quality Issues Amid the Evolving Electrical Demands Facing Utilities," Energies, MDPI, vol. 16(21), pages 1-23, October.

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