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Dynamic IFFSM Modeling Using IFHMM-Based Bayesian Non-parametric Learning for Energy Disaggregation in Smart Solar Home System

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Broadband Communications, Networks, and Systems (Broadnets 2019)

Abstract

Recently, the analysis and recognition of each appliance’s energy consumption are fundamental in smart homes and smart buildings systems. Our paper presents a novel Non-Intrusive Load Monitoring (NILM) recognition method based on Bayesian Non-Parametric (BNP) learning approach to solve the problem of energy disaggregation for smart Solar Home System (SHS). Several researches assumed that there is prior information about the household appliances in order to restrict those that do not hold the maximum expectation for inference. Therefore, to deal with the unknown number of electrical appliances in a SHS, we have adapted a dynamic Infinite Factorial Hidden Markov Model (IFHMM) -based Infinite Factorial Finite State Machine (IFFSM) to our NILM times-series modeling as an unsupervised BNP learning method. Our suggested method can grip with few or nappropriate learning data as well as to standardize electrical appliance modeling. Our proposed method outperforms FHMM-based FSM modeling results illustrated in literature.

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Author information

Authors and Affiliations

  1. Ecole Nationale d’Ingénieurs de Tunis, Innov’Com Laboratory, Université Tunis El Manar, Tunis, Tunisia

    Kalthoum Zaouali

  2. Department of Electrical and Computer Engineering, Laval University, Quebec, Canada

    Mohamed Lassaad Ammari

  3. Chifco Company, Tunis, Tunisia

    Mohamed Lassaad Ammari & Amine Chouaieb

  4. Sup’Com, Innov’Com Laboratory, Carthage University, Tunis, Tunisia

    Amine Chouaieb & Ridha Bouallegue

Authors
  1. Kalthoum Zaouali
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  2. Mohamed Lassaad Ammari
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  3. Amine Chouaieb
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  4. Ridha Bouallegue
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Corresponding author

Correspondence to Kalthoum Zaouali .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Qingshan Li

  2. Xidian University, Xi'an, China

    Shengli Song

  3. Xidian University, Xi'an, China

    Rui Li

  4. Xidian University, Xi'an, China

    Yueshen Xu

  5. Xi’an Jiaotong University, Xi'an, Shaanxi, China

    Wei Xi

  6. Shanghai University, Shanghai, China

    Honghao Gao

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zaouali, K., Ammari, M.L., Chouaieb, A., Bouallegue, R. (2019). Dynamic IFFSM Modeling Using IFHMM-Based Bayesian Non-parametric Learning for Energy Disaggregation in Smart Solar Home System. In: Li, Q., Song, S., Li, R., Xu, Y., Xi, W., Gao, H. (eds) Broadband Communications, Networks, and Systems. Broadnets 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-030-36442-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-36442-7_11

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