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|Organization=16th European Conference, EvoApplications 2013
 
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|Abstract=Various changes in energy production and consumption lead to new challenges for design and control mechanisms of the energy system. In particular, the intermittent nature of power generation from renewables asks for signi�cantly increased load  
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|Abstract=Various changes in energy production and consumption lead to new challenges for design and control mechanisms of the energy system. In particular, the intermittent nature of power generation from renewables asks for significantly increased load  
 
flexibility to support local balancing of energy demand and supply. This paper focuses on a flexible, generic
 
flexibility to support local balancing of energy demand and supply. This paper focuses on a flexible, generic
energy management system for Smart Buildings in real-world applications, which is already in use in households and offi�ce buildings. The major contribution is the design of a plug-and-play-type Evolutionary Algorithm for optimizing distributed generation, storage and consumption using a sub-problem based approach. Relevant power consuming or producing components identify themselves as sub-problems by providing
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energy management system for Smart Buildings in real-world applications, which is already in use in households and office buildings. The major contribution is the design of a plug-and-play-type Evolutionary Algorithm for optimizing distributed generation, storage and consumption using a sub-problem based approach. Relevant power consuming or producing components identify themselves as sub-problems by providing
an abstract speci�cation of their genotype, an evaluation function and a back transformation from an optimized genotype to speci�c control commands. The generic optimization respects technical constraints as well as external signals like variable energy tari�s. The relevance of this approach to energy optimization is evaluated in di�fferent scenarios. Results show signifcant improvements of self-consumption rates and reductions of energy costs.
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an abstract specification of their genotype, an evaluation function and a back transformation from an optimized genotype to specific control commands. The generic optimization respects technical constraints as well as external signals like variable energy tariffs. The relevance of this approach to energy optimization is evaluated in different scenarios. Results show signifcant improvements of self-consumption rates and reductions of energy costs.
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|Projekt=IZEUS
 
|Forschungsgruppe=Effiziente Algorithmen
 
|Forschungsgruppe=Effiziente Algorithmen
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{{Forschungsgebiet Auswahl
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|Forschungsgebiet=Energieinformatik
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|Forschungsgebiet=Evolutionäre Algorithmen
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|Forschungsgebiet=Organic Computing
 
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Aktuelle Version vom 24. Oktober 2016, 15:41 Uhr


Customizable Energy Management in Smart Buildings using Evolutionary Algorithms




Published: 2014 April

Buchtitel: Applications of Evolutionary Computation: 16th European Conference, EvoApplications 2013, Granada, Spain, April 2014, Proceedings
Verlag: Springer
Organisation: 16th European Conference, EvoApplications 2013

Referierte Veröffentlichung

BibTeX




Kurzfassung
Various changes in energy production and consumption lead to new challenges for design and control mechanisms of the energy system. In particular, the intermittent nature of power generation from renewables asks for significantly increased load flexibility to support local balancing of energy demand and supply. This paper focuses on a flexible, generic energy management system for Smart Buildings in real-world applications, which is already in use in households and office buildings. The major contribution is the design of a plug-and-play-type Evolutionary Algorithm for optimizing distributed generation, storage and consumption using a sub-problem based approach. Relevant power consuming or producing components identify themselves as sub-problems by providing an abstract specification of their genotype, an evaluation function and a back transformation from an optimized genotype to specific control commands. The generic optimization respects technical constraints as well as external signals like variable energy tariffs. The relevance of this approach to energy optimization is evaluated in different scenarios. Results show signifcant improvements of self-consumption rates and reductions of energy costs.


Projekt

IZEUS


Verknüpfte Tools

Energy Smart Home Lab, Organic Smart Home


Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet

Evolutionäre Algorithmen, Organic Computing, Energieinformatik