Run-time parameter selection and tuning for energy optimization algorithms

被引：0
|
作者：
Mauser, Ingo [1 ]
Dorscheid, Marita [2 ]
Schmeck, Hartmut [2 ]
机构：
[1] FZI Research Center for Information Technology, Karlsruhe,76131, Germany
[2] Karlsruhe Institute of Technology – Institute AIFB, Karlsruhe,76128, Germany
来源：
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2014年 / 8672卷
关键词：
Risk perception - Energy management systems - Evolutionary algorithms - Parameter estimation;
D O I：
10.1007/978-3-319-10762-2_8
中图分类号：
学科分类号：
摘要：
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 showreduced run-timeswith similar or even improved solution quality, while the distributed application reduces the risk of over-confidence and over-tuning. © Springer International Publishing Switzerland 2014.
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页码：80 / 89
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