<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1664</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>797</pageFirst>
    <pageLast>806</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-04-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Electricity Load Forecasting - An Evaluation of Simple 1D-CNN Network Structures</title>
    <abstract language="eng">This paper presents a convolutional neural network (CNN)which can be used for forecasting electricity load profiles 36 hours intothe future. In contrast to well established CNN architectures, the inputdata is one-dimensional. A parameter scanning of network parameters isconducted in order to gain information about the influence of the kernelsize,  number  of  filters,  and  dense  size.  The  results  show  that  a  goodforecast quality can already be achieved with basic CNN architectures.The  method  works  not  only  for  smooth  sum  loads  of  many  hundredconsumers, but also for the load of apartment buildings</abstract>
    <parentTitle language="eng">International Conference on Time Series and Forecasting (ITISE 2019), Proceedings of Papers Vol. 2, 25-27 September 2019, Granada (Spain)</parentTitle>
    <identifier type="url">https://arxiv.org/abs/1911.11536</identifier>
    <identifier type="url">http://itise.ugr.es/ITISE2019_vol2.pdf</identifier>
    <identifier type="isbn">978-84-17970-78-9</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-16649</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Christian Lang</author>
    <author>Florian Steinborn</author>
    <author>Oliver Steffens</author>
    <author>Elmar Wolfgang Lang</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>energy load forecasting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>STLF</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CNN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>con-volutional networks</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="institutes" number="FakANK">Fakultät Angewandte Natur- und Kulturwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="16312">Energie und Mobilität</collection>
    <collection role="institutes" number="">Labor Bauphysik</collection>
    <thesisPublisher>Ostbayerische Technische Hochschule Regensburg</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-oth-regensburg/files/1664/Electricity_Load_Forecasting.pdf</file>
  </doc>
  <doc>
    <id>2033</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>205</pageFirst>
    <pageLast>218</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Applying a 1D-CNN Network to Electricity Load Forecasting</title>
    <abstract language="eng">This paper presents a convolutional neural network (CNN) which can be used for forecasting electricity load profiles 36 hours into the future. In contrast to well established CNN architectures, the input data is one-dimensional. A parameter scanning of network parameters is conducted in order to gain information about the influence of the kernel size, number of filters and number of nodes. Furthermore, different dropout methods are applied to the CNN and are evaluated. The results show that a good forecast quality can already be achieved with basic CNN architectures, the dropout improves the forecast. The method works not only for smooth sum loads of many hundred consumers, but also for the load of single apartment buildings.</abstract>
    <parentTitle language="eng">Theory and Applications of Time Series Analysis</parentTitle>
    <identifier type="doi">10.1007/978-3-030-56219-9_14</identifier>
    <enrichment key="ConferenceStatement">International Conference on Time Series and Forecasting, ITISE 2019</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Christian Lang</author>
    <author>Florian Steinborn</author>
    <author>Oliver Steffens</author>
    <author>Elmar Wolfgang Lang</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Energy load forecasting</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>STLF</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CNN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Convolutional networks</value>
    </subject>
    <collection role="institutes" number="FakANK">Fakultät Angewandte Natur- und Kulturwissenschaften</collection>
    <collection role="othforschungsschwerpunkt" number="16312">Energie und Mobilität</collection>
    <collection role="institutes" number="">Labor Bauphysik</collection>
  </doc>
</export-example>
