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Originalveröffentlichung
DOI: 10.1145/3208903.3208915

Towards the Modeling of Flexibility Using Artificial Neural Networks in Energy Management and Smart Grids: Note

Förderer, Kevin; Ahrens, Mischa; Bao, Kaibin; Mauser, Ingo; Schmeck, Hartmut

Abstract (englisch):
This paper presents a novel approach to the representation and communication of the energy flexibility of distributed energy resources. The approach uses artificial neural networks (ANNs) to represent the devices and act as surrogate models. The main benefit of this approach is its potential to represent arbitrary energy flexibilities and the resulting universal applicability in various usage patterns, some of which are presented in detail in this paper. Furthermore, the flexibility represented by an ANN can be conditioned on the state of the corresponding devices and their environment, such that only a small state update needs to be communicated to construct feasible load profiles by a third party. Therefore, in contrast to other approaches, such as support vector data description, new ANNs only need to be constructed once the device configuration changes.


Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Jahr 2018
Sprache Englisch
Identifikator ISBN: 978-1-4503-5767-8
KITopen ID: 1000084352
HGF-Programm 37.06.01; LK 01
Erschienen in Proceedings of the 9th International Conference on Future Energy Systems (ACM e-Energy 2018), Karlsruhe, 12.-15. Juni 2018
Verlag ACM, New York, NY
Seiten 85–90
Serie e-Energy ’18
Schlagworte Machine Learning, Deep learning, Energy Informatics
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