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Methods, controllers and systems for the control of distribution systems using a neural network architecture
A deep approximation neural network architecture is described which extrapolates data over unseen states for demand response applications in order to control distribution systems like product distribution systems of which energy distribution systems, e.g. heat or electrical power distribution, are one example. The present invention describes a model-free control technique mainly in the form of Reinforcement Learning (RL) whereby a controller learns from interaction with the system to be controlled to control product distribution s of which energy distribution systems, e.g. heat or electrical power distribution, are one example.
Jaar aanvraag: 2016
Jaar toekenning: 2018
Gevalideerd voor IOF-sleutel: Ja
Toegewezen aan: Universitaire Associatie Brussel