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State identification of home appliance with transient features in residential buildings

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Abstract

Nonintrusive load monitoring (NILM) is crucial for extracting patterns of electricity consumption of household appliance that can guide users’ behavior in using electricity while their privacy is respected. This study proposes an online method based on the transient behavior of individual appliances as well as system steady-state characteristics to estimate the operating states of the appliances. It determines the number of states for each appliance using the density-based spatial clustering of applications with noise (DBSCAN) method and models the transition relationship among different states. The states of the working appliances are identified from aggregated power signals using the Kalman filtering method in the factorial hidden Markov model (FHMM). Thereafter, the identified states are confirmed by the verification of system states, which are the combination of the working states of individual appliances. The verification step involves comparing the total measured power consumption with the total estimated power consumption. The use of transient features can achieve fast state inference and it is suitable for online load disaggregation. The proposed method was tested on a high-resolution data set such as Labeled hIgh-Frequency daTaset for Electricity Disaggregation (LIFTED) and it outperformed other related methods in the literature.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Illinois Institute of Technology, Chicago, IL, 60616, USA

    Lei Yan, Runnan Xu, Mehrdad Sheikholeslami & Zuyi Li

  2. School of Electrical Engineering, Northeast Electric Power University, Jilin, 132012, China

    Lei Yan, Runnan Xu, Mehrdad Sheikholeslami, Yang Li & Zuyi Li

Authors
  1. Lei Yan
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  2. Runnan Xu
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  3. Mehrdad Sheikholeslami
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  4. Yang Li
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  5. Zuyi Li
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Correspondence to Zuyi Li.

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Yan, L., Xu, R., Sheikholeslami, M. et al. State identification of home appliance with transient features in residential buildings. Front. Energy 16, 130–143 (2022). https://doi.org/10.1007/s11708-022-0822-z

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  • DOI: https://doi.org/10.1007/s11708-022-0822-z

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