Kernel selection for power market inference via block successive upper bound minimization

Advanced data analytics are undoubtedly needed to enable the envisioned smart grid functionalities. Towards that goal, modern statistical learning tools are developed for day-ahead electricity market inference. Congestion patterns are modeled as rank-one components in the matrix of spatio-temporal prices. The new kernel-based predictor is regularized by the square root of the nuclear norm of the sought matrix. Such a regularizer not only promotes low-rank solutions, but it also facilitates a systematic kernel selection methodology. The non-convex optimization problem involved is efficiently driven to a stationary point following a block successive upper bound minimization approach. Numerical tests on real high-dimensional market data corroborate the interpretative merits and the computational efficiency of the novel method.