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Uncovering Hidden Factors in Electricity Consumption Based on Gaussian Mixture Estimation

Author

Listed:
  • Shiwen Liao

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

  • Lu Wei

    (Department of Computer Science, Texas Tech University, Lubbock, TX 79409, USA)

  • Wencong Su

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

Abstract

Load characteristics play an essential role in the planning of power generation and distribution. Various undiscovered factors, which could be socioeconomic, geographic, or climatic, make it possible to describe the electricity demand by a multimodal distribution. This letter proposes a novel method based on multimodal distributions to characterize the hidden factors in electricity consumption. Consequently, a new approach is developed to evaluate the impact of the underlying factors of electricity consumption. Some quantifiable and predictable factors are analyzed in developing multimodal distribution to describe the expected demand. Simulations based on synthetic and real-world data have been conducted to demonstrate the usefulness and robustness of the proposed method.

Suggested Citation

  • Shiwen Liao & Lu Wei & Wencong Su, 2022. "Uncovering Hidden Factors in Electricity Consumption Based on Gaussian Mixture Estimation," Energies, MDPI, vol. 15(1), pages 1-6, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:319-:d:716910
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    References listed on IDEAS

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    1. Schilling M.F. & Watkins A.E. & Watkins W., 2002. "Is Human Height Bimodal?," The American Statistician, American Statistical Association, vol. 56, pages 223-229, August.
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