  The transmission-constrained unit commitment (TC-UC) problem is one of the
most relevant problems solved by independent system operators for the daily
operation of power systems. Given its computational complexity, this problem is
usually not solved to global optimality for real-size power systems. In this
paper, we propose a data-driven method that leverages historical information to
screen out network constraints in the TC-UC problem. First, past data on demand
and renewable generation throughout the network are used to learn the
congestion status of transmission lines. Then, we infer the lines that will not
become congested for upcoming operating conditions based on such learning and
disregard their capacity constraints. This way, we formulate a reduced TC-UC
problem that is easier to solve. Numerical results on a medium- and a
large-size power system show that the proposed approach outperforms existing
ones by significantly reducing the computational time while obtaining solutions
that are equal or close to the one obtained with the original TC-UC problem.
Furthermore, the purely data-driven method we propose can be seamlessly
complemented with a constraint generation procedure to guarantee that the
optimal solution to the original TC-UC problem is eventually recovered.
