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  <doc>
    <id>715</id>
    <completedYear>2017</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-06-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Appliance Usage Prediction for the Smart Home with an Application to Energy Demand Side Management - And Why Accuracy is not a Good Performance Metric for this Problem.</title>
    <abstract language="eng">Shifting energy peak load is a subject that plays a huge role in the currently changing energy market, where renewable energy sources no longer produce the exact amount of energy demanded. &#13;
&#13;
Matching demand to supply requires behavior Changes on the customerside, which can be achieved by incentives suchas Real-Time-Pricing (RTP). &#13;
Various studies show that such incentives cannot be utilized without a complexity reduction, e.g., by smart home automation systems that inform the customer about possible savings or automatically schedule appliances to off-peak load phases.&#13;
&#13;
 We propose a probabilistic appliance usage prediction based on historical energy data that can be used to identify the times of day where an appliance will be used and therefore make load shift recommendations that suite the customer’s usage proﬁle. A huge issue is how to provide a valid performance evaluation for this particular problem. &#13;
&#13;
We will argue why the commonly used accuracy metric is not suitable, and suggest to use other metrics like the area under the Receiver Operating Characteristic (ROC) curve, Matthews Correlation Coefﬁcient (MCC) or F1-Score instead.</abstract>
    <parentTitle language="eng">6th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS)</parentTitle>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Marc Wenninger</author>
    <author>Jochen Schmidt</author>
    <author>Toni Goeller</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Real Time Pricing (RTP)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Household Appliance Usage Prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Demand Side Management</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
  </doc>
  <doc>
    <id>833</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>231</pageFirst>
    <pageLast>237</pageLast>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>SciTePress</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2018-05-23</completedDate>
    <publishedDate>2018-06-04</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards Cost-Effective Utility Business Models - Selecting a Communication Architecture for the Rollout of New Smart Energy Services</title>
    <abstract language="eng">The IT architecture for meter reading and utility services is at the core of new business models and has a decisive role as an enabler for resource efficiency measures. The communication architecture used by those services has significant impact on cost, flexibility and speed of new service rollout. This article describes how the dominant system model for meter reading came about, what alternative models exist, and what trade-offs those models have for rollout of new services by different stakeholders. Control of a self learning home automation system by dynamic tariff information (Real-Time-Pricing) is given as an application example.</abstract>
    <parentTitle language="eng">Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS</parentTitle>
    <identifier type="isbn">978-989-758-292-9</identifier>
    <identifier type="urn">urn:nbn:de:bvb:861-opus4-8332</identifier>
    <enrichment key="RS_Acronym">LV-Selbstlernend</enrichment>
    <enrichment key="RS_Correlation">Ja</enrichment>
    <enrichment key="RS_ProjectTitle">Erhöhung des Lastverschiebungspotenzials durch selbstlernende Home Automation Algorithmen mit flexiblen Optimierungskriterien</enrichment>
    <enrichment key="RS_FundingAgency">BMBF</enrichment>
    <enrichment key="RS_GrantNumber">01LY1506</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Toni Goeller</author>
    <author>Marc Wenninger</author>
    <author>Jochen Schmidt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Smart Meter, Advanced Metering Infrastructure, AMI</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="institutes" number="">Fakultät für Informatik</collection>
    <thesisPublisher>Technische Hochschule Rosenheim</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-rosenheim/files/833/paper.pdf</file>
  </doc>
</export-example>
