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|Title=Adaptive Building Energy Management with Multiple Commodities and Flexible Evolutionary Optimization
 
|Title=Adaptive Building Energy Management with Multiple Commodities and Flexible Evolutionary Optimization
|Year=2015
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|Year=2016
 
|Journal=Renewable Energy
 
|Journal=Renewable Energy
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|Volume=87, Part 2
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|Pages=911-921
 
|Publisher=Elsevier
 
|Publisher=Elsevier
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|Note=Optimization Methods in Renewable Energy Systems Design
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|Tool=Organic Smart Home
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{{Publikation Tool
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{{Publikation Details
 
{{Publikation Details
|Abstract=Abstract To enable the efficient utilization of energy carriers and the successful integration of renewable energies into energy systems, building energy management systems (BEMS) are inevitable. In this article, we present a modular BEMS and its customizable architecture that enable a flexible approach towards the optimization of building operation. The system is capable of handling the energy flows in the building and across all energy carriers as well as the interdependencies between devices, while keeping a unitized approach towards devices and the optimization of their operation. Evaluations in realistic scenarios show the ability of the BEMS to increase energy efficiency, self-consumption, and self-sufficiency as well as to reduce energy consumption and costs by an improved scheduling of the devices that considers all energy carriers in buildings as well as their interdependencies.  
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|Abstract=Abstract To enable the efficient utilization of energy carriers and the successful integration of renewable energies into energy systems, building energy management systems (BEMS) are inevitable. In this article, we present a modular BEMS and its customizable architecture that enable a flexible approach towards the optimization of building operation. The system is capable of handling the energy flows in the building and across all energy carriers as well as the interdependencies between devices, while keeping a unitized approach towards devices and the optimization of their operation. Evaluations in realistic scenarios show the ability of the BEMS to increase energy efficiency, self-consumption, and self-sufficiency as well as to reduce energy consumption and costs by an improved scheduling of the devices that considers all energy carriers in buildings as well as their interdependencies.
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|ISSN=0960-1481
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|DOI Name=10.1016/j.renene.2015.09.003
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|Projekt=Energie-Allianz, IZEUS
 
|Forschungsgruppe=Effiziente Algorithmen
 
|Forschungsgruppe=Effiziente Algorithmen
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}}
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{{Forschungsgebiet Auswahl
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|Forschungsgebiet=Energieinformatik
 
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Aktuelle Version vom 20. Januar 2017, 09:30 Uhr


Adaptive Building Energy Management with Multiple Commodities and Flexible Evolutionary Optimization


Adaptive Building Energy Management with Multiple Commodities and Flexible Evolutionary Optimization



Veröffentlicht: 2016

Journal: Renewable Energy

Seiten: 911-921
Verlag: Elsevier
Volume: 87, Part 2
Bemerkung: Optimization Methods in Renewable Energy Systems Design

Referierte Veröffentlichung

BibTeX







Kurzfassung
Abstract To enable the efficient utilization of energy carriers and the successful integration of renewable energies into energy systems, building energy management systems (BEMS) are inevitable. In this article, we present a modular BEMS and its customizable architecture that enable a flexible approach towards the optimization of building operation. The system is capable of handling the energy flows in the building and across all energy carriers as well as the interdependencies between devices, while keeping a unitized approach towards devices and the optimization of their operation. Evaluations in realistic scenarios show the ability of the BEMS to increase energy efficiency, self-consumption, and self-sufficiency as well as to reduce energy consumption and costs by an improved scheduling of the devices that considers all energy carriers in buildings as well as their interdependencies.

ISSN: 0960-1481
DOI Link: 10.1016/j.renene.2015.09.003

Projekt

Energie-AllianzIZEUS


Verknüpfte Tools

Energy Smart Home Lab, Organic Smart Home


Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet

Energieinformatik