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International Journal of Energy Research
Article . 2016 . Peer-reviewed
License: Wiley Online Library User Agreement
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Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids

Consumption modeling Markov chains and Bayesian networks for DSM design
Authors: Roje, Tomislav; Marín, Luis G.; Sáez Hueichapan, Doris; Orchard Concha, Marcos; Jiménez Estévez, Guillermo;

Consumption modeling based on Markov chains and Bayesian networks for a demand side management design of isolated microgrids

Abstract

Summary This paper proposes a novel simulator of energy consumption patterns that allows designing demand side management (DSM) strategies without economic incentives. The simulator emulates consumers' patterns with and without installed DSM interfaces, based on both actual consumption measurements and surveys applied to the inhabitants of an existing isolated microgrid (Huatacondo, Chile) that has a particular DSM strategy without economic incentives. The simulator uses Markov chains to generate data characterizing consumption patterns without DSM and Bayesian networks for cases in which the users respond to the DSM strategy. Data obtained from the simulator are used to derive a response model of the consumers to the DSM interface, which can be included for the energy management system design. Results show that the implemented strategy can be effective and can generate savings up to 4.45% in diesel consumption for an ideal case where all the dwellings have the interface installed. Copyright © 2016 John Wiley & Sons, Ltd.

Country
Chile
Related Organizations
Keywords

Bayesian network, Microgrid, Markov chain, Demand side management

  • BIP!
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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).
    9
    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.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation 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!
9
Top 10%
Average
Average
Green
gold