Hierarchical Multiplicative Model for Characterizing Residential Electricity Consumption

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Abstract

This work presents a hierarchical multiplicative framework for modeling the energy consumption of households. The constituents of the model are a lognormally distributed annual consumption, an annual consumption profile at weekly resolution, a mean weekly consumption profile, and a multiplicative lognormally distributed random variation. Further, the annual and weekly profiles of households are shown to fall naturally into a small number of rather homogeneous groups, identified by the regular decomposition method. The framework is adapted to monitor and compare populations of electricity consumers. On the other hand, it provides a convenient way to produce synthetic traces of household energy consumption with similar stochastic properties as measured traces. It is also shown how additional household information can be utilized to predict both the annual consumption and the random variation of the consumption of a household.
Original languageEnglish
Number of pages14
JournalJournal of Energy Engineering
Volume144
Issue number3
Early online date15 Mar 2018
DOIs
Publication statusPublished - Mar 2018
MoE publication typeA1 Journal article-refereed

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Energy utilization
Electricity
Decomposition
household energy
electricity
consumption
electricity consumption
decomposition
household
modeling
energy consumption

Keywords

  • Household electricity consumption
  • mathematical modeling
  • clustering
  • profiles
  • monitoring

Cite this

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title = "Hierarchical Multiplicative Model for Characterizing Residential Electricity Consumption",
abstract = "This work presents a hierarchical multiplicative framework for modeling the energy consumption of households. The constituents of the model are a lognormally distributed annual consumption, an annual consumption profile at weekly resolution, a mean weekly consumption profile, and a multiplicative lognormally distributed random variation. Further, the annual and weekly profiles of households are shown to fall naturally into a small number of rather homogeneous groups, identified by the regular decomposition method. The framework is adapted to monitor and compare populations of electricity consumers. On the other hand, it provides a convenient way to produce synthetic traces of household energy consumption with similar stochastic properties as measured traces. It is also shown how additional household information can be utilized to predict both the annual consumption and the random variation of the consumption of a household.",
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Hierarchical Multiplicative Model for Characterizing Residential Electricity Consumption. / Kuusela, Pirkko; Norros, Ilkka; Reittu, Hannu; Piira, Kalevi.

In: Journal of Energy Engineering, Vol. 144, No. 3, 03.2018.

Research output: Contribution to journalArticleScientificpeer-review

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AB - This work presents a hierarchical multiplicative framework for modeling the energy consumption of households. The constituents of the model are a lognormally distributed annual consumption, an annual consumption profile at weekly resolution, a mean weekly consumption profile, and a multiplicative lognormally distributed random variation. Further, the annual and weekly profiles of households are shown to fall naturally into a small number of rather homogeneous groups, identified by the regular decomposition method. The framework is adapted to monitor and compare populations of electricity consumers. On the other hand, it provides a convenient way to produce synthetic traces of household energy consumption with similar stochastic properties as measured traces. It is also shown how additional household information can be utilized to predict both the annual consumption and the random variation of the consumption of a household.

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