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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. | 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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Version vom 15. Oktober 2014, 12:12 Uhr
Customizable Energy Management in Smart Buildings using Evolutionary Algorithms
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.
Energy Smart Home Lab, Organic Smart Home
Evolutionäre Algorithmen, Organic Computing, Energieinformatik