### 抄録

Residential energy demand varies widely in terms of time-series behaviors, amounts consumed between families, and even within one family. Residential energy demand profiles have a high degree of uncertainty in their essentials because the demand profile is entirely based on the occupant-driven load. When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, in order to clarify rational energy system selection guidelines and rational operation strategies, authors aim to extract basic demand time-series patterns from two kinds of measured demand (electricity and domestic hot water), measured over 26307 days of data in Japan. Authors also aim to reveal the relationship between primary energy consumption and demand patterns. Demand time-series data are categorized by means of a kind of "unsupervised" learning, which is a hierarchical clustering method using a statistical pseudo-distance. The statistical pseudo-distance is calculated from the generalized Kullback-Leibler divergence with the Gaussian mixture distribution fitted to the demand time-series data. The classified demand patterns are built using a hierarchical clustering and then a comparison is performed between the optimal operation of the two systems (a polymer electrolyte membrane fuel cell co-generation system, and a CO_{2} heat pump system) and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the demand profiles appropriately built. Our results show that basic demand patterns are extracted by the proposed method. The demand patterns, the amount of daily demand and heat-to-power ratio of demand affect the primary energy reduction ratio of the polymer electrolyte membrane fuel cell co-generation system.

元の言語 | English |
---|---|

ホスト出版物のタイトル | Proceedings of the 25th International Conference on Efficiency, Cost, Optimization and Simulation of Energy Conversion Systems and Processes, ECOS 2012 |

出版者 | Aabo Akademi University |

ページ | 1-16 |

ページ数 | 16 |

巻 | 3 |

ISBN（印刷物） | 9788866553229 |

出版物ステータス | Published - 2012 |

イベント | 25th International Conference on Efficiency, Cost, Optimization and Simulation of Energy Conversion Systems and Processes, ECOS 2012 - Perugia 継続期間: 2012 6 26 → 2012 6 29 |

### Other

Other | 25th International Conference on Efficiency, Cost, Optimization and Simulation of Energy Conversion Systems and Processes, ECOS 2012 |
---|---|

市 | Perugia |

期間 | 12/6/26 → 12/6/29 |

### Fingerprint

### ASJC Scopus subject areas

- Energy(all)
- Environmental Science(all)

### これを引用

*Proceedings of the 25th International Conference on Efficiency, Cost, Optimization and Simulation of Energy Conversion Systems and Processes, ECOS 2012*(巻 3, pp. 1-16). Aabo Akademi University.

**A comparison of optimal operation of residential energy systems using clustered demand patterns based on kullback-leibler divergence.** / Yoshida, Akira; Amano, Yoshiharu; Murata, Noboru; Ito, Koichi; Hashizume, Takumi.

研究成果: Conference contribution

*Proceedings of the 25th International Conference on Efficiency, Cost, Optimization and Simulation of Energy Conversion Systems and Processes, ECOS 2012.*巻. 3, Aabo Akademi University, pp. 1-16, 25th International Conference on Efficiency, Cost, Optimization and Simulation of Energy Conversion Systems and Processes, ECOS 2012, Perugia, 12/6/26.

}

TY - GEN

T1 - A comparison of optimal operation of residential energy systems using clustered demand patterns based on kullback-leibler divergence

AU - Yoshida, Akira

AU - Amano, Yoshiharu

AU - Murata, Noboru

AU - Ito, Koichi

AU - Hashizume, Takumi

PY - 2012

Y1 - 2012

N2 - Residential energy demand varies widely in terms of time-series behaviors, amounts consumed between families, and even within one family. Residential energy demand profiles have a high degree of uncertainty in their essentials because the demand profile is entirely based on the occupant-driven load. When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, in order to clarify rational energy system selection guidelines and rational operation strategies, authors aim to extract basic demand time-series patterns from two kinds of measured demand (electricity and domestic hot water), measured over 26307 days of data in Japan. Authors also aim to reveal the relationship between primary energy consumption and demand patterns. Demand time-series data are categorized by means of a kind of "unsupervised" learning, which is a hierarchical clustering method using a statistical pseudo-distance. The statistical pseudo-distance is calculated from the generalized Kullback-Leibler divergence with the Gaussian mixture distribution fitted to the demand time-series data. The classified demand patterns are built using a hierarchical clustering and then a comparison is performed between the optimal operation of the two systems (a polymer electrolyte membrane fuel cell co-generation system, and a CO2 heat pump system) and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the demand profiles appropriately built. Our results show that basic demand patterns are extracted by the proposed method. The demand patterns, the amount of daily demand and heat-to-power ratio of demand affect the primary energy reduction ratio of the polymer electrolyte membrane fuel cell co-generation system.

AB - Residential energy demand varies widely in terms of time-series behaviors, amounts consumed between families, and even within one family. Residential energy demand profiles have a high degree of uncertainty in their essentials because the demand profile is entirely based on the occupant-driven load. When evaluating residential energy systems like co-generation systems, hot water and electricity demand profiles are critical. In this paper, in order to clarify rational energy system selection guidelines and rational operation strategies, authors aim to extract basic demand time-series patterns from two kinds of measured demand (electricity and domestic hot water), measured over 26307 days of data in Japan. Authors also aim to reveal the relationship between primary energy consumption and demand patterns. Demand time-series data are categorized by means of a kind of "unsupervised" learning, which is a hierarchical clustering method using a statistical pseudo-distance. The statistical pseudo-distance is calculated from the generalized Kullback-Leibler divergence with the Gaussian mixture distribution fitted to the demand time-series data. The classified demand patterns are built using a hierarchical clustering and then a comparison is performed between the optimal operation of the two systems (a polymer electrolyte membrane fuel cell co-generation system, and a CO2 heat pump system) and the operation of a reference system (a conventional combination of a condensing gas boiler and electricity purchased from the grid) using the demand profiles appropriately built. Our results show that basic demand patterns are extracted by the proposed method. The demand patterns, the amount of daily demand and heat-to-power ratio of demand affect the primary energy reduction ratio of the polymer electrolyte membrane fuel cell co-generation system.

KW - Co-generation

KW - Demand pattern

KW - Gaussian mixture model

KW - Hierarchical clustering

KW - KL-divergence

KW - Optimal operation

UR - http://www.scopus.com/inward/record.url?scp=84896374681&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84896374681&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84896374681

SN - 9788866553229

VL - 3

SP - 1

EP - 16

BT - Proceedings of the 25th International Conference on Efficiency, Cost, Optimization and Simulation of Energy Conversion Systems and Processes, ECOS 2012

PB - Aabo Akademi University

ER -