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Generation of Time-of-Use Tariffs for Demand Side Management using Artificial Neural Networks

Ahrens, Mischa; Schmeck, Hartmut

Abstract (englisch):
This poster proposes a new method to generate individual time-of-use electricity tariffs to exploit the flexibility of energy prosumers while preserving privacy and minimizing communication effort as well as computational cost. Since an employed tariff structure may be impossible to derive analytically from a particular behavior of a prosumer, artificial neural networks may be used to learn the underlying mechanisms implicitly based on simulated household data. Using the acquired knowledge, such a network could be able to generate suitable tariffs to achieve a desired behavior.

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: 1000084351
HGF-Programm 37.06.01 (POF III, 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 Press, New York, NY
Seiten 396–398
Schlagworte Maschine Learning, Deep Learning, Energy Informatics
Nachgewiesen in Scopus
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