260 citations · 296 across the 6 of their papers we have counts for
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eess.SY2020★ 17 cited
State space models for building control: how deep should you go?
Baptiste Schubnel, Rafael E. Carrillo, Paolo Taddeo +4
Power consumption in buildings show non-linear behaviors that linear models cannot capture whereas recurrent neural networks (RNNs) can. This ability makes RNNs attractive alternat…
eess.SY2020★ 17 cited
A hybrid learning method for system identification and optimal control
Baptiste Schubnel, Rafael E. Carrillo, Pierre-Jean Alet +1
We present a three-step method to perform system identification and optimal control of non-linear systems. Our approach is mainly data driven and does not require active excitation…