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The unit commitment problem is an important optimization problem in theenergy industry used to compute the most economical operating schedules ofpower plants. Typically, this problem has to be solved repeatedly withdifferent data but with the same problem structure. Machine learning techniqueshave been applied in this context to find primal feasible solutions. On theother hand, Dantzig-Wolfe decomposition with a column generation procedure hasbeen shown to be successful in solving the unit commitment problem to tighttolerance. We propose the use of machine learning models not to find primalfeasible solutions directly but to generate initial dual values for the columngeneration procedure. Our numerical experiments compare machine learning basedmethods for warmstarting the column generation procedure with three baselines:column pre-population, the linear programming relaxation and coldstart. Theexperiments reveal that the machine learning approaches are able to find bothtight lower bounds and accurate primal feasible solutions in a shorter timecompared to the baselines. Furthermore, these approaches scale well to handlelarge instances.

Use of Machine Learning Models to Warmstart Column Generation for Unit Commitment