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Verlagsausgabe
DOI: 10.5445/IR/1000086895
Veröffentlicht am 24.10.2018
Originalveröffentlichung
DOI: 10.1186/s42162-018-0024-4

Modeling flexibility using artificial neural networks

Förderer, Kevin; Ahrens, Mischa; Bao, Kaibin; Mauser, Ingo; Schmeck, Hartmut

Abstract (englisch):
The flexibility of distributed energy resources (DERs) can be modeled in various ways. Each model that can be used for creating feasible load profiles of a DER represents a potential model for the flexibility of that particular DER. Based on previous work, this paper presents generalized patterns for exploiting such models. Subsequently, the idea of using artificial neural networks in such patterns is evaluated. We studied different types and topologies of ANNs for the presented realization patterns and multiple device configurations, achieving a remarkably precise representation of the given devices in most of the cases. Overall, there was no single best ANN topology. Instead, a suitable individual topology had to be found for every pattern and device configuration. In addition to the best performing ANNs for each pattern and configuration that is presented in this paper all data from our experiments is published online. The paper is concluded with an evaluation of a classification based pattern using data of a real combined heat and power plant in a smart building.


Zugehörige Institution(en) am KIT Forschungszentrum Informatik, Karlsruhe (FZI)
Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Kompetenzzentrum für angewandte Sicherheitstechnologie (KASTEL)
Publikationstyp Zeitschriftenaufsatz
Jahr 2018
Sprache Englisch
Identifikator ISSN: 2520-8942
URN: urn:nbn:de:swb:90-868959
KITopen ID: 1000086895
Erschienen in Energy Informatics
Band S1
Heft 21
Seiten 73-91
Projektinformation C/sells (BMWi, 03SIN123)
ENSURE (BMBF, 03SFK1A)
grid-control (BMWi, 03ET7539F)
KASTEL_SVI (BMBF, 16KIS0521)
Bemerkung zur Veröffentlichung 7th DACH+ Conference on Energy Informatics, Oldenburg, Germany, 11-12 October 2018
Vorab online veröffentlicht am 10.10.2018
Schlagworte Smart Grid; Flexibility Modeling; Distributed energy resources; Demand side management; Machine learning
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