  The unit commitment problem is an important optimization problem in the
energy industry used to compute the most economical operating schedules of
power plants. Typically, this problem has to be solved repeatedly with
different data but with the same problem structure. Machine learning techniques
have been applied in this context to find primal feasible solutions. On the
other hand, Dantzig-Wolfe decomposition with a column generation procedure has
been shown to be successful in solving the unit commitment problem to tight
tolerance. We propose the use of machine learning models not to find primal
feasible solutions directly but to generate initial dual values for the column
generation procedure. Our numerical experiments compare machine learning based
methods for warmstarting the column generation procedure with three baselines:
column pre-population, the linear programming relaxation and coldstart. The
experiments reveal that the machine learning approaches are able to find both
tight lower bounds and accurate primal feasible solutions in a shorter time
compared to the baselines. Furthermore, these approaches scale well to handle
large instances.
