  Managing supply and demand in the electricity grid is becoming more
challenging due to the increasing penetration of variable renewable energy
sources. As significant end-use consumers, and through better grid integration,
buildings are expected to play an expanding role in the future smart grid.
Predictive control allows buildings to better harness available energy
flexibility from the building passive thermal mass. However, due to the
heterogeneous nature of the building stock, developing computationally
tractable control-oriented models, which adequately represent the complex and
nonlinear thermal-dynamics of individual buildings, is proving to be a major
hurdle. Data-driven predictive control, coupled with the "Internet of Things",
holds the promise for a scalable and transferrable approach,with data-driven
models replacing traditional physics-based models. This review examines recent
work utilising data-driven predictive control for demand side management
application with a special focus on the nexus of model development and control
integration, which to date, previous reviews have not addressed. Further topics
examined include the practical requirements for harnessing passive thermal mass
and the issue of feature selection. Current research gaps are outlined and
future research pathways are suggested to identify the most promising
data-driven predictive control techniques for grid integration of buildings.
