  Grid-interactive building control is a challenging and important problem for
reducing carbon emissions, increasing energy efficiency, and supporting the
electric power grid. Currently researchers and practitioners are confronted
with a choice of control strategies ranging from model-free (purely
data-driven) to model-based (directly incorporating physical knowledge) to
hybrid methods that combine data and models. In this work, we identify
state-of-the-art methods that span this methodological spectrum and evaluate
their performance for multi-zone building HVAC control in the context of three
demand response programs. We demonstrate, in this context, that hybrid methods
offer many benefits over both purely model-free and model-based methods as long
as certain requirements are met. In particular, hybrid controllers are
relatively sample efficient, fast online, and high accuracy so long as the test
case falls within the distribution of training data. Like all data-driven
methods, hybrid controllers are still subject to generalization errors when
applied to out-of-sample scenarios. Key takeaways for control strategies are
summarized and the developed software framework is open-sourced.
