A comparison of optimal operation of a residential fuel cell co-generation system using clustered demand patterns based on Kullback-Leibler divergence

Akira Yoshida, Yoshiharu Amano, Noboru Murata, Koichi Ito, Takumi Hasizume

    Research output: Contribution to journalArticle

    7 Citations (Scopus)

    Abstract

    When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, the authors aim to extract basic time-series demand patterns from two kinds of measured demand (electricity and domestic hot water), and also aim to reveal effective demand patterns for primary energy saving. Time-series demand data are categorized with a hierarchical clustering method using a statistical pseudo-distance, which is represented by the generalized Kullback-Leibler divergence of two Gaussian mixture distributions. The classified demand patterns are built using hierarchical clustering and then a comparison is made between the optimal operation of a polymer electrolyte membrane fuel cell co-generation system and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the appropriately built demand profiles. Our results show that basic demand patterns are extracted by the proposed method, and the heat-to-power ratio of demand, the amount of daily demand, and demand patterns affect the primary energy saving of the co-generation system.

    Original languageEnglish
    Pages (from-to)374-399
    Number of pages26
    JournalEnergies
    Volume6
    Issue number1
    DOIs
    Publication statusPublished - 2013 Jan

    Fingerprint

    Kullback-Leibler Divergence
    Fuel Cell
    Fuel cells
    Electricity
    Time series
    Energy conservation
    Proton exchange membrane fuel cells (PEMFC)
    Boilers
    Water
    Hierarchical Clustering
    Energy Saving
    Gases
    Demand
    Mixture Distribution
    Gaussian Mixture
    Electrolyte
    Clustering Methods
    Gaussian distribution
    Membrane
    Polymers

    Keywords

    • Co-generation
    • Demand pattern
    • Gaussian mixture model
    • Hierarchical clustering
    • KL-divergence
    • Optimal operation

    ASJC Scopus subject areas

    • Computer Science(all)

    Cite this

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    title = "A comparison of optimal operation of a residential fuel cell co-generation system using clustered demand patterns based on Kullback-Leibler divergence",
    abstract = "When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, the authors aim to extract basic time-series demand patterns from two kinds of measured demand (electricity and domestic hot water), and also aim to reveal effective demand patterns for primary energy saving. Time-series demand data are categorized with a hierarchical clustering method using a statistical pseudo-distance, which is represented by the generalized Kullback-Leibler divergence of two Gaussian mixture distributions. The classified demand patterns are built using hierarchical clustering and then a comparison is made between the optimal operation of a polymer electrolyte membrane fuel cell co-generation system and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the appropriately built demand profiles. Our results show that basic demand patterns are extracted by the proposed method, and the heat-to-power ratio of demand, the amount of daily demand, and demand patterns affect the primary energy saving of the co-generation system.",
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    author = "Akira Yoshida and Yoshiharu Amano and Noboru Murata and Koichi Ito and Takumi Hasizume",
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    AU - Yoshida, Akira

    AU - Amano, Yoshiharu

    AU - Murata, Noboru

    AU - Ito, Koichi

    AU - Hasizume, Takumi

    PY - 2013/1

    Y1 - 2013/1

    N2 - When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, the authors aim to extract basic time-series demand patterns from two kinds of measured demand (electricity and domestic hot water), and also aim to reveal effective demand patterns for primary energy saving. Time-series demand data are categorized with a hierarchical clustering method using a statistical pseudo-distance, which is represented by the generalized Kullback-Leibler divergence of two Gaussian mixture distributions. The classified demand patterns are built using hierarchical clustering and then a comparison is made between the optimal operation of a polymer electrolyte membrane fuel cell co-generation system and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the appropriately built demand profiles. Our results show that basic demand patterns are extracted by the proposed method, and the heat-to-power ratio of demand, the amount of daily demand, and demand patterns affect the primary energy saving of the co-generation system.

    AB - When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, the authors aim to extract basic time-series demand patterns from two kinds of measured demand (electricity and domestic hot water), and also aim to reveal effective demand patterns for primary energy saving. Time-series demand data are categorized with a hierarchical clustering method using a statistical pseudo-distance, which is represented by the generalized Kullback-Leibler divergence of two Gaussian mixture distributions. The classified demand patterns are built using hierarchical clustering and then a comparison is made between the optimal operation of a polymer electrolyte membrane fuel cell co-generation system and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the appropriately built demand profiles. Our results show that basic demand patterns are extracted by the proposed method, and the heat-to-power ratio of demand, the amount of daily demand, and demand patterns affect the primary energy saving of the co-generation system.

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    KW - Demand pattern

    KW - Gaussian mixture model

    KW - Hierarchical clustering

    KW - KL-divergence

    KW - Optimal operation

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