Published: 2018 Juni
Buchtitel: Proceedings of the Ninth International Conference on Future Energy Systems (e-Energy '18)
Erscheinungsort: New York, NY, USA
Organisation: Ninth International Conference on Future Energy Systems (e-Energy '18), ACM
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.
DOI Link: 10.1145/3208903.3212037