  The potential of Model Predictive Control in buildings has been shown many
times, being successfully used to achieve various goals, such as minimizing
energy consumption or maximizing thermal comfort. However, mass deployment has
thus far failed, in part because of the high engineering cost of obtaining and
maintaining a sufficiently accurate model. This can be addressed by using
adaptive data-driven approaches. The idea of using behavioral systems theory
for this purpose has recently found traction in the academic community. In this
study, we compare variations thereof with different amounts of data used,
different regularization weights, and different methods of data selection.
Autoregressive models with exogenous inputs (ARX) are used as a
well-established reference. All methods are evaluated by performing iterative
system identification on two long-term data sets from real occupied buildings,
neither of which include artificial excitation for the purpose of system
identification. We find that: (1) Sufficient prediction accuracy is achieved
with all methods. (2) The ARX models perform slightly better, while having the
additional advantages of fewer tuning parameters and faster computation. (3)
Adaptive and non-adaptive schemes perform similarly. (4) The regularization
weights of the behavioral systems theory methods show the expected trade-off
characteristic with an optimal middle value. (5) Using the most recent data
yields better performance than selecting data with similar weather as the day
to be predicted. (6) More data improves the model performance.
