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 and Takumi Hasizume
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Akira Yoshida: Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, 162-0044, Tokyo, Japan
Yoshiharu Amano: Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, 162-0044, Tokyo, Japan
Noboru Murata: School of Science and Engineering, Waseda University, 1-4-3 Okubo, Shinjuku-ku, 169-8555, Tokyo, Japan
Koichi Ito: Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, 162-0044, Tokyo, Japan
Takumi Hasizume: Research Institute for Science and Engineering, Waseda University, 17 Kikui-cho, Shinjuku-ku, 162-0044, Tokyo, Japan

Energies, 2013, vol. 6, issue 1, 1-26

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.

Keywords: co-generation; demand pattern; Gaussian mixture model; hierarchical clustering; KL-divergence; optimal operation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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