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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JDS</journal-id>
      <journal-title-group>
        <journal-title>Journal of Data Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">1680-743X</issn>
      <issn pub-type="ppub">1680-743X</issn>
      <publisher>
        <publisher-name>SOSRUC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">203_OK</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.201801_16(1).0001</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>A Method for Evaluating Options for Motif Detection in Electricity Meter Data</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Dent</surname>
            <given-names>Ian</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">School of Computer Science Nottingha University</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Craig</surname>
            <given-names>Tony</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">The James Hutton Institute</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Aickelin</surname>
            <given-names>Uwe</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Rodden</surname>
            <given-names>Tom</given-names>
          </name>
        </contrib>
      </contrib-group>
      <volume>16</volume>
      <issue>1</issue>
      <fpage>1</fpage>
      <lpage>28</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Investigation of household electricity usage patterns, and mat- ching the patterns to behaviours, is an important area of research given the centrality of such patterns in addressing the needs of the electricity indu- stry. Additional knowledge of household behaviours will allow more effective targeting of demand side management (DSM) techniques. This paper addresses the question as to whether a reasonable number of meaningful motifs, that each represent a regular activity within a domestic household, can be identified solely using the household level electricity meter data. Using UK data collected from several hundred households in Spring 2011 monitored at a frequency of five minutes, a process for finding repeating short patterns (motifs) is defined. Different ways of representing the motifs exist and a qualitative approach is presented that allows for choosing between the options based on the number of regular behaviours detected (neither too few nor too many).</p>
      </abstract>
      <kwd-group>
        <label>Keywords</label>
        <kwd>Motif detection</kwd>
        <kwd>Clustering</kwd>
        <kwd>Electricity Usage</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
