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    <id>34098</id>
    <completedYear/>
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    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>655</pageFirst>
    <pageLast>671</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>106</volume>
    <type>articlenr</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2024-02-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Aggregated wind power characteristic curves and artificial intelligence for the regional wind power infeed estimation</title>
    <abstract language="eng">The wind power generation is highly dependent on current weather conditions. In the course of the energy transition, the generation levels from volatile wind energy are constantly increasing. Accordingly, the prediction of regional wind power generation is a particularly important and challenging task due to the highly distributed installations. This paper presents a study on the role of regional wind power infeed estimation and proposes a multi-aggregated wind power characteristics model based on three scaled Gumbel distribution functions. Multi-levels of wind turbines and their allocation are investigated for the regional aggregated wind power. Relative peak power performance and full load hours are compared for the proposed model and the real measurement obtained from a local distribution system operator. Furthermore, artificial intelligence technologies using neural networks, such as Long Short-Term Memory (LSTM), stacked LSTM and CNN–LSTM, are investigated by using different historical measurement as input data. The results show that the suggested stacked LSTM performs stably and reliably in regional power prediction.</abstract>
    <parentTitle language="eng">Electrical Engineering</parentTitle>
    <identifier type="issn">0948-7921</identifier>
    <identifier type="issn">1432-0487</identifier>
    <identifier type="doi">10.1007/s00202-023-02005-z</identifier>
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    <author>
      <firstName>Yang</firstName>
      <lastName>Li</lastName>
    </author>
    <author>
      <firstName>Przemysław</firstName>
      <lastName>Janik</lastName>
    </author>
    <author>
      <firstName>Harald</firstName>
      <lastName>Schwarz</lastName>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Artificial intelligence</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Aggregated wind power characteristics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Regional wind power</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Engineering</value>
    </subject>
    <collection role="Import" number="import">Import</collection>
    <collection role="institutes" number="3201H01">FG Dezentrale Energiesysteme</collection>
  </doc>
  <doc>
    <id>32369</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>601</pageFirst>
    <pageLast>613</pageLast>
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    <issue>3</issue>
    <volume>71</volume>
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    <completedDate>2024-01-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Proposal of a regional cluster model for analysis of electrical power network performance</title>
    <parentTitle language="eng">Archives of Electrical Engineering</parentTitle>
    <identifier type="doi">10.24425/aee.2022.141673</identifier>
    <identifier type="issn">2300-2506</identifier>
    <identifier type="issn">1427-4221</identifier>
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    <enrichment key="Fprofil">1 Energiewende und Dekarbonisierung / Energy Transition and Decarbonisation</enrichment>
    <author>
      <firstName>Yang</firstName>
      <lastName>Li</lastName>
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    <submitter>
      <firstName>Klaus</firstName>
      <lastName>Pfeiffer</lastName>
    </submitter>
    <author>
      <firstName>Przemysław</firstName>
      <lastName>Janik</lastName>
    </author>
    <author>
      <firstName>Klaus</firstName>
      <lastName>Pfeiffer</lastName>
    </author>
    <author>
      <firstName>Harald</firstName>
      <lastName>Schwarz</lastName>
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    <collection role="institutes" number="3202H01">FG Energieverteilung und Hochspannungstechnik</collection>
    <collection role="institutes" number="3201H01">FG Dezentrale Energiesysteme</collection>
  </doc>
  <doc>
    <id>32372</id>
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    <publishedYear>2022</publishedYear>
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    <language>eng</language>
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    <volume>Vol. 7(42)</volume>
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    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-01-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Charging and discharging strategies for clustered regional energy storage system</title>
    <parentTitle language="eng">Power Electronics and Drives</parentTitle>
    <identifier type="doi">10.2478/pead-2022-0005</identifier>
    <identifier type="issn">2543-4292</identifier>
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    <author>
      <firstName>Yang</firstName>
      <lastName>Li</lastName>
    </author>
    <submitter>
      <firstName>Klaus</firstName>
      <lastName>Pfeiffer</lastName>
    </submitter>
    <author>
      <firstName>Przemysław</firstName>
      <lastName>Janik</lastName>
    </author>
    <author>
      <firstName>Klaus</firstName>
      <lastName>Pfeiffer</lastName>
    </author>
    <author>
      <firstName>Harald</firstName>
      <lastName>Schwarz</lastName>
    </author>
    <collection role="institutes" number="3202H01">FG Energieverteilung und Hochspannungstechnik</collection>
    <collection role="institutes" number="3201H01">FG Dezentrale Energiesysteme</collection>
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