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Disaggregating power consumption of commercial buildings based on the finite mixture model

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  • Zhou, Yang
  • Shi, Zhixiong
  • Shi, Zhengyu
  • Gao, Qing
  • Wu, Libo

Abstract

Power disaggregation that breaks down the overall power consumption to appliance level acts as a feasible technical solution to meet the extensive data demand but reduce the costs of installing advanced metering system in Demand Side Management (DSM). Considering the intensive query of high-frequency training data of existing methods, this paper presents a new behavior based model applicable to low-frequency data by introducing external determining factors of power consumption into a finite mixture model (FMM) that disaggregates overall power consumption into those of various electrical appliances. Empirical verification by employing a dataset including detailed hourly appliance-level power consumption of commercial buildings in Shanghai proves that this newly developed model can provide more accurate result than other previous models but requires relatively lower-frequency data. The benefits of energy-saving potential from information feedback and appliance replacement facilitated by disaggregation data is further simulated to show the practical application.

Suggested Citation

  • Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.
    • Handle: RePEc:eee:appene:v:243:y:2019:i:c:p:35-46
    DOI: 10.1016/j.apenergy.2019.03.014
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    Cited by:

    1. Pamulapati, Trinadh & Mallipeddi, Rammohan & Lee, Minho, 2020. "Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling," Applied Energy, Elsevier, vol. 267(C).
    2. Shi, Zhengyu & Wu, Libo & Zhou, Yang, 2023. "Predicting household energy consumption in an aging society," Applied Energy, Elsevier, vol. 352(C).
    3. Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2020. "Data-driven evaluation of HVAC operation and savings in commercial buildings," Applied Energy, Elsevier, vol. 278(C).
    4. Jia, Mengshuo & Huang, Shaowei & Wang, Zhiwen & Shen, Chen, 2021. "Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error," Renewable Energy, Elsevier, vol. 163(C), pages 1318-1332.
    5. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
    6. Seuk Yen Phoong & Shi Ling Khek & Seuk Wai Phoong, 2022. "The Bibliometric Analysis on Finite Mixture Model," SAGE Open, , vol. 12(2), pages 21582440221, May.
    7. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).

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