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

Published: 2018 Juni
Herausgeber: ACM
Buchtitel: Proceedings of the Ninth International Conference on Future Energy Systems (e-Energy '18)
Seiten: 85-90
Verlag: ACM
Erscheinungsort: New York, NY, USA
Organisation: Ninth International Conference on Future Energy Systems (e-Energy '18), ACM
Referierte Veröffentlichung

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.

ISBN: 978-1-4503-5767-8

DOI Link: 10.1145/3208903.3208915




Effiziente Algorithmen

Deep Learning, Maschinelles Lernen, Energieinformatik, Modellierung