  A class of data-driven control methods has recently emerged based on Willems'
fundamental lemma. Such methods can ease the modelling burden in control design
but can be sensitive to disturbances acting on the system under control. In
this paper, we propose a restructuring of the problem to incorporate segmented
prediction trajectories. The proposed segmentation leads to reduced tracking
error for longer prediction horizons in the presence of unmeasured disturbance
and noise when compared to an unsegmented formulation. The performance
characteristics are illustrated in a set-point tracking case study in which the
segmented formulation enables more consistent performance over a wide range of
prediction horizons. The method is then applied to a building energy management
problem using a detailed simulation environment. The case studies show that
good tracking performance is achieved for a range of horizon choices, whereas
performance degrades with longer horizons without segmentation.
