  The smart grid vision entails advanced information technology and data
analytics to enhance the efficiency, sustainability, and economics of the power
grid infrastructure. Aligned to this end, modern statistical learning tools are
leveraged here for electricity market inference. Day-ahead price forecasting is
cast as a low-rank kernel learning problem. Uniquely exploiting the market
clearing process, congestion patterns are modeled as rank-one components in the
matrix of spatio-temporally varying prices. Through a novel nuclear norm-based
regularization, kernels across pricing nodes and hours can be systematically
selected. Even though market-wide forecasting is beneficial from a learning
perspective, it involves processing high-dimensional market data. The latter
becomes possible after devising a block-coordinate descent algorithm for
solving the non-convex optimization problem involved. The algorithm utilizes
results from block-sparse vector recovery and is guaranteed to converge to a
stationary point. Numerical tests on real data from the Midwest ISO (MISO)
market corroborate the prediction accuracy, computational efficiency, and the
interpretative merits of the developed approach over existing alternatives.
