Electricity Customer Clustering Following Experts' Principle for Demand Response Applications
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- @Article{kang:2015:Energies,
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author = "Jimyung Kang and Jee-Hyong Lee",
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title = "Electricity Customer Clustering Following Experts'
Principle for Demand Response Applications",
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journal = "Energies",
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year = "2015",
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volume = "8",
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number = "10",
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keywords = "genetic algorithms, genetic programming, electricity
customer clustering, load profile, demand response",
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ISSN = "1996-1073",
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URL = "https://www.mdpi.com/1996-1073/8/10/12242",
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DOI = "doi:10.3390/en81012242",
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abstract = "The clustering of electricity customers might have an
effective meaning if, and only if, it is verified by
domain experts. Most of the previous studies on
customer clustering, however, do not consider real
applications, but only the structure of clusters.
Therefore, there is no guarantee that the clustering
results are applicable to real domains. In other words,
the results might not coincide with those of domain
experts. In this paper, we focus on formulating
clusters that are applicable to real applications based
on domain expert knowledge. More specifically, we try
to define a distance between customers that generates
clusters that are applicable to demand response
applications. First, the k-sliding distance, which is a
new distance between two electricity customers, is
proposed for customer clustering. The effect of
k-sliding distance is verified by expert knowledge.
Second, a genetic programming framework is proposed to
automatically determine a more improved distance
measure. The distance measure generated by our
framework can be considered as a reflection of the
clustering principles of domain experts. The results of
the genetic programming demonstrate the possibility of
deriving clustering principles.",
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notes = "also known as \cite{en81012242}",
- }
Genetic Programming entries for
Jimyung Kang
Jee-Hyong Lee
Citations