Aus Aifbportal
Version vom 24. Oktober 2016, 15:41 Uhr von Bt0473 (Diskussion | Beiträge)
(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu:Navigation, Suche

Run-Time Parameter Selection and Tuning for Energy Optimization Algorithms

Run-Time Parameter Selection and Tuning for Energy Optimization Algorithms

Published: 2014

Buchtitel: Parallel Problem Solving from Nature–PPSN XIII
Seiten: 80-89
Verlag: Springer International Publishing

Referierte Veröffentlichung


Energy Management Systems (EMS) promise a great potential to enable the sustainable and efficient integration of distributed energy generation from renewable sources by optimization of energy flows. In this paper, we present a run-time selection and meta-evolutionary parameter tuning component for optimization algorithms in EMS and an approach for the distributed application of this component. These have been applied to an existing EMS, which uses an Evolutionary Algorithm. Evaluations of the component in realistic scenarios show reduced run-times with similar or even improved solution quality, while the distributed application reduces the risk of over-confidence and over-tuning.



Verknüpfte Tools

Energy Smart Home Lab, Organic Smart Home


Effiziente Algorithmen


Evolutionäre Algorithmen, Organic Computing, Energieinformatik