Experimental analysis of data-driven control for a building heating
  system
  Driven by the opportunity to harvest the flexibility related to building
climate control for demand response applications, this work presents a
data-driven control approach building upon recent advancements in reinforcement
learning. More specifically, model assisted batch reinforcement learning is
applied to the setting of building climate control subjected to a dynamic
pricing. The underlying sequential decision making problem is cast on a markov
decision problem, after which the control algorithm is detailed. In this work,
fitted Q-iteration is used to construct a policy from a batch of experimental
tuples. In those regions of the state space where the experimental sample
density is low, virtual support samples are added using an artificial neural
network. Finally, the resulting policy is shaped using domain knowledge. The
control approach has been evaluated quantitatively using a simulation and
qualitatively in a living lab. From the quantitative analysis it has been found
that the control approach converges in approximately 20 days to obtain a
control policy with a performance within 90% of the mathematical optimum. The
experimental analysis confirms that within 10 to 20 days sensible policies are
obtained that can be used for different outside temperature regimes.
