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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Energyarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Energy
Article . 2016 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Gas networks simulation from disaggregation of low frequency nodal gas consumption

Authors: M. H. Fazel Zarandi; S. Askari; N Montazerin;

Gas networks simulation from disaggregation of low frequency nodal gas consumption

Abstract

Abstract Natural gas distribution network aims at delivering sufficient gas flow to consumers at consumption nodes. A gas meter is installed at each consumption node of gas network which records low frequency monthly gas consumption of that node. There is also a gas meter in city gate station which records high frequency daily gas consumption of the entire network. Gas distribution companies need to know daily gas consumption of each node (consumer) and daily nodal pressure of the network for control and planning purposes. Gas consumption of each node is a time series with constant recording time step. This article presents a time series disaggregation method for related time series with identical recording times. This method disaggregates low frequency gas consumption at the nodes to high frequency consumption such that sum of consumption of all nodes in each day equals daily consumption of the entire network measured by the city gate station gas meter in that day. These high frequency data are high frequency profile of consumers' gas consumption and can give analysis of the network. The article also discusses compatibility of low frequency consumption of gas network and city gate station high frequency data and the required corrections when these data are mismatched in mathematical sense. Finally, the method is applied to a gas distribution network and results are presented and discussed. The proposed method is general and can be applied to any set of related or unrelated time series and any network with known governing equations as well as the gas network.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average