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{{Publikation Erster Autor
 
{{Publikation Erster Autor
|ErsterAutorNachname=Rios
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|ErsterAutorNachname=Rios Silva
|ErsterAutorVorname=Fredy
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|ErsterAutorVorname=Fredy Hernan
 
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{{Phdthesis
 
{{Phdthesis
 
|Title=Stigmergy-Based Load Scheduling in a Demand Side Management Context
 
|Title=Stigmergy-Based Load Scheduling in a Demand Side Management Context
|Instructor=Hartmut Schmeck
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|Instructor=Hartmut Schmeck; Wolf Fichtner
 
|Date=2016/06/09
 
|Date=2016/06/09
 
|School=KIT, Fakultät für Wirtschaftswissenschaften
 
|School=KIT, Fakultät für Wirtschaftswissenschaften
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{{Publikation Details
 
{{Publikation Details
|Forschungsgruppe=Effiziente Algorithmen
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|Abstract=This work proposes an approach, based on a fundamental coordination mechanism from nature, namely stigmergy. The proposed meta-heuristic is utilized to distributively calculate global schedules for a population of customers provided with intelligent devices. These schedules maximize renewable energy sources utilization. Furthermore, this approach is adapted and utilized as a coordination mechanism of autonomous customers to modify their consumption behavior in a real-time optimization context.
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|DOI Name=10.5445/IR/1000055801
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|Forschungsgruppe=Effiziente Algorithmen/en
 
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{{Forschungsgebiet Auswahl
 
{{Forschungsgebiet Auswahl
 
|Forschungsgebiet=Energieinformatik
 
|Forschungsgebiet=Energieinformatik
 
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Aktuelle Version vom 4. August 2021, 16:47 Uhr

Stigmergy-Based Load Scheduling in a Demand Side Management Context




Datum: 9. Juni 2016
KIT, Fakultät für Wirtschaftswissenschaften
Erscheinungsort / Ort: Karlsruhe
Referent(en): Hartmut Schmeck, Wolf Fichtner
BibTeX


Kurzfassung
This work proposes an approach, based on a fundamental coordination mechanism from nature, namely stigmergy. The proposed meta-heuristic is utilized to distributively calculate global schedules for a population of customers provided with intelligent devices. These schedules maximize renewable energy sources utilization. Furthermore, this approach is adapted and utilized as a coordination mechanism of autonomous customers to modify their consumption behavior in a real-time optimization context.

DOI Link: 10.5445/IR/1000055801



Forschungsgruppe

Effiziente Algorithmen/en


Forschungsgebiet

Energieinformatik