Electricity Customer Clustering Following Experts' Principle for Demand Response Applications

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@Article{kang:2015:Energies,

• author = "Jimyung Kang and Jee-Hyong Lee",
• title = "Electricity Customer Clustering Following Experts' Principle for Demand Response Applications",
• journal = "Energies",
• year = "2015",
• volume = "8",
• number = "10",
• keywords = "genetic algorithms, genetic programming, electricity customer clustering, load profile, demand response",
• ISSN = "1996-1073",
• URL = "https://www.mdpi.com/1996-1073/8/10/12242",
• DOI = "doi:10.3390/en81012242",
• 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.",
• notes = "also known as \cite{en81012242}",

}

Genetic Programming entries for Jimyung Kang Jee-Hyong Lee

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