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.
|Original language||English (US)|
|Number of pages||12|
|Journal||IEEE Journal on Selected Topics in Signal Processing|
|State||Published - Dec 1 2014|
Bibliographical notePublisher Copyright:
© 2007-2012 IEEE.
- Block-coordinate descent
- day-ahead energy prices
- graph Laplacian
- kernel-based learning
- low-rank matrix
- multi-kernel learning
- nuclear norm regularization