  Data-driven control approaches for the minimization of energy consumption of
buildings have the potential to significantly reduce deployment costs and
increase uptake of advanced control in this sector. A number of recent
approaches based on the application of Willems' fundamental lemma for
data-driven controller design from input/output measurements are very promising
for deterministic LTI systems. This paper \change{proposes a systematic way to
handle unknown measurement noise and measurable process noise}, and extends
these data-driven control schemes to adaptive building control via a robust
bi-level formulation, whose upper level ensures robustness and whose lower
level guarantees prediction quality. Corresponding numerical improvements and
an active excitation mechanism are proposed to enable a computationally
efficient reliable operation. The efficacy of the proposed scheme is validated
by \change{a multi-zone building simulation} and a real-world experiment on a
single-zone conference building on the EPFL campus. The real-world experiment
includes a 20-day non-stop test, where, without extra modeling effort, our
proposed controller improves 18.4\% energy efficiency against an
industry-standard controller, while also robustly ensuring occupant comfort.
