  Data-enabled predictive control (DeePC) is a recently established form of
Model Predictive Control (MPC), based on behavioral systems theory. While
eliminating the need to explicitly identify a model, it requires an additional
regularization with a corresponding weight to function well with noisy data.
The tuning of this weight is non-trivial and has a significant impact on
performance. In this paper, we compare three reformulations of DeePC that
either eliminate the regularization, or simplify the tuning to a trivial point.
A building simulation study shows a comparable performance for all three
reformulations of DeePC. However, a conventional MPC with a black-box model
slightly outperforms them, while solving much faster, and yielding smoother
optimal trajectories. Two of the DeePC variants also show sensitivity to an
unobserved biased input noise, which is not present in the conventional MPC.
